Verified Tech Jobs & Hiring Companies, Updated Every 24 Hours

ROLE OVERVIEW We are looking for a seasoned Data Architect with deep expertise in enterprise data warehousing, ETL/ELT pipeline development, and business intelligence. You will be responsible for the end-to-end design and implementation of our data architecture — from data ingestion and cleansing to dimensional modeling, warehouse construction, and BI dashboard delivery. You will also establish robust data governance frameworks including data catalogs, lineage tracking, and security audit systems. This role requires strong hands-on capability combined with architectural vision, and prior experience in complex industries such as finance, international trade, or manufacturing is highly valued. KEY RESPONSIBILITIES Data Acquisition, Cleansing & Integration Design and implement enterprise-wide data collection strategies across heterogeneous source systems (ETRM, ERP, CRM, external APIs, flat files, and databases). Develop robust data cleansing and standardization pipelines to ensure data accuracy, consistency, and completeness across all data sources. Build data integration frameworks to consolidate data from disparate systems into a unified enterprise data warehouse, handling schema evolution and data drift. Implement Change Data Capture (CDC) mechanisms and incremental data loading strategies to ensure near real-time data freshness. Data Warehouse Architecture & Modeling Lead the design and construction of the enterprise data warehouse (EDW) using industry-proven methodologies (Inmon, Kimball, or Data Vault). Apply dimensional modeling expertise to design star schemas, snowflake schemas, and constellation models optimized for analytical query performance. architect data marts for specific business domains (finance, sales, supply chain, operations) with clear separation of concerns. Select and implement appropriate data warehouse technologies based on workload characteristics: batch analytics (Hive, Spark SQL), real-time analytics (ClickHouse, Apache Doris, StarRocks), or MPP databases (Greenplum, Vertica). Design layered data architectures (ODS → DWD → DWS → ADS / Bronze → Silver → Gold) with clear data flow and transformation logic at each layer. ETL/ELT Pipeline Development Design, build, and maintain scalable ETL/ELT pipelines using Apache Spark, Flink, Hive, MapReduce, or proprietary ETL tools (Informatica, Talend, Kettle). Implement workflow orchestration using Apache Airflow, DolphinScheduler, Azkaban, or Oozie to schedule, monitor, and manage data pipelines. Establish data quality frameworks: automated validation rules, anomaly detection, data profiling, and quality scorecards to ensure pipeline reliability and data trustworthiness. Build comprehensive monitoring and alerting systems for pipeline health, data freshness, and SLA compliance. Optimize pipeline performance through partitioning, bucketing, indexing, and query tuning strategies. Business Intelligence & Reporting Develop enterprise BI reporting systems and interactive dashboards using tools such as Apache Superset, FineBI, Tableau, Power BI, or Quick BI. Design executive dashboards, operational reports, and self-service analytics interfaces tailored to business stakeholders across finance, trading, operations, and management. Implement metrics frameworks (KPIs, OKRs) and ensure data definitions are standardized, documented, and consistently applied across all reports. Work directly with business units to understand analytical requirements and translate them into effective data models and visualizations. Data Governance & Standards Design and implement enterprise data governance frameworks from the ground up, including data ownership policies, stewardship roles, and governance workflows. Build a centralized data catalog (using Apache Atlas, DataHub, Amundsen, or similar) documenting all data assets, business glossaries, and metadata. Implement data lineage analysis systems to track data flow from source to consumption, enabling impact analysis and root cause diagnosis. Establish data security and audit frameworks: access control (RBAC/ABAC), data masking, encryption policies, sensitive data discovery, and compliance audit trails. Define and enforce data standards: naming conventions, data dictionaries, master data management (MDM) rules, and data quality SLAs. Ensure regulatory compliance with data protection laws (GDPR, PIPL, industry-specific regulations) through governance controls and documentation. REQUIRED QUALIFICATIONS Bachelor's degree or above in Computer Science, Statistics, Mathematics, Information Systems, or related technical field. 3+ years of professional experience in data development, data engineering, or data architecture roles, with a proven track record of building production data warehouse systems. Expert-level proficiency in SQL (complex queries, window functions, CTEs, query optimization) and Python for data processing and pipeline development. Mastery of data warehouse modeling theories and methodologies: dimensional modeling (Kimball), normalized modeling (Inmon), and Data Vault 2.0. Demonstrated experience designing star schemas, snowflake schemas, and aggregate tables for complex business domains. Deep domain experience in at least one complex industry: financial services (banking, securities, insurance), international trade (import/export, supply chain, logistics), or manufacturing (production planning, quality control, industrial IoT). Understanding of industry-specific data entities, business processes, and analytical requirements. Strong hands-on experience with ETL/ELT tools and frameworks: Apache Spark (batch and streaming), Apache Flink, Hive, MapReduce, Sqoop, DataX, SeaTunnel, or commercial ETL platforms (Informatica PowerCenter, Talend, IBM DataStage). Proficiency in workflow orchestration: Apache Airflow, DolphinScheduler, Azkaban, Oozie, or equivalent scheduling and monitoring platforms. Production experience with at least one major data warehouse/analytics database: ClickHouse, Apache Doris, StarRocks, Hive, Greenplum, Vertica, Teradata, or Snowflake. Strong hands-on experience with BI and visualization tools: Apache Superset, FineBI, Tableau, Power BI, Quick BI, or similar enterprise BI platforms. Deep expertise in Data Governance with demonstrated ability to build governance systems from scratch: data catalog implementation, metadata management, automated lineage tracking (using OpenLineage, Marquez, or similar), data quality monitoring, and security audit frameworks. Solid understanding of relational and analytical databases: PostgreSQL, MySQL, Oracle, SQL Server, Greenplum — including performance tuning, indexing strategies, and query optimization. Experience with big data ecosystem components: HDFS, YARN, ZooKeeper, Kafka, and Hadoop distributions (CDH, HDP, Apache). PREFERRED QUALIFICATIONS Experience with master data management (MDM) platforms and methodologies. Familiarity with data mesh or data fabric architectural patterns for large-scale decentralized data environments. Experience with data versioning and time-travel capabilities in modern data warehouses. Domain knowledge on Energy Trading, Market Risk Management. Professional certifications: CDMP (Certified Data Management Professional), DAMA-DMBOK, or vendor-specific data platform certifications. Any personal data you share with us during the application process will be processed strictly in compliance with applicable data protection laws and our Privacy Notice .

Securing the Future with AvePoint AvePoint is a global leader in data management and governance, trusted by over 21,000 customers worldwide to enhance their digital workplaces across Microsoft, Google, Salesforce, and other collaboration platforms. Our global channel partner program includes more than 3,500 managed service providers, value-added resellers, and systems integrators, with our solutions featured in over 100 cloud marketplaces. To learn more, visit www.avepoint.com . At AvePoint, we are dedicated to investing in our people. Our culture, driven by agility, passion, and teamwork, empowers you to shape your career, make a significant impact, and take ownership of your future. Discover how you can unleash your potential with us! Key Responsibilities Analyze data needs and document technical requirements. Migrate data collection processes to more efficient channels. Plan, design, and implement data engineering jobs and reporting solutions to meet analytics needs. Develop test plans and scripts for system testing and support user acceptance testing. Collaborate with technical teams to ensure smooth deployment and adoption of new solutions. Ensure the smooth operation and performance of IT solutions, including addressing production issues. What We Are Looking For Strong understanding of waterfall/Agile methodologies. Hands-on experience with DevOps deployment and data virtualization tools such as Denodo (preferred). Proficiency with reporting or visualization tools like SAP BO and Tableau. Experience working with Hive, Impala, and Cloudera Data Platform is preferred. Expertise in big data engineering, using Python, Pyspark, Linux, and ETL tools like Informatica. Strong SQL skills and experience in data modeling and analysis. Knowledge of analytics and data warehouse implementations. Ability to troubleshoot complex technical issues. Experience in high-availability, high-performance systems hosted in data centers or hybrid cloud environments is a plus. Any personal data you share with us during the application process will be processed strictly in compliance with applicable data protection laws and our Privacy Notice. Any personal data you share with us during the application process will be processed strictly in compliance with applicable data protection laws and our Privacy Notice .

About AvePoint: Securing the Future. AvePoint is a global leader in data management and data governance, and over 21,000 customers worldwide rely on our solutions to modernize the digital workplace across Microsoft, Google, Salesforce and other collaboration environments. AvePoint’s global channel partner program includes over 3,500 managed service providers, value added resellers and systems integrators, with our solutions available in more than 100 cloud marketplaces. To learn more, visit www.avepoint.com . At AvePoint, we are committed to investing in our people. Agility, passion and teamwork set us up to do our best work and foster a culture where you are empowered to craft your career, make an impact, and own (y)our future. Unleash the power of you! About the role: We are seeking a highly skilled and experienced Big Data Engineer to join our team. The ideal candidate must have a minimum of 4 years of experience managing data engineering jobs in big data environment e.g., Cloudera Data Platform. Overall experience can be 4 to 10 years . Consultant/ Senior Consultant preferably. The successful candidate will be responsible for designing, developing, and maintaining the data ingestion and processing jobs as per business requirements. Candidate will also be integrating data sets to provide seamless data access to users. Key responsibilities: Analyze the Authority’s data needs and document the requirements. Refine data collection/consumption by migrating data collection to more efficient channels. Plan, design and implement data engineering jobs and reporting solutions to meet the analytical needs. Develop test plan and scripts for system testing, support user acceptance testing. Work with the Authority’s technical teams to ensure smooth deployment and adoption of new solution. Ensure the smooth operations and service level of IT solutions. Support production issues - Track record in implementing systems with high availability, high performance, high security. The ideal candidate has: Good understanding and completion of projects using waterfall/Agile methodology. Good understanding of analytics and data warehouse implementations. Ability to troubleshoot complex issues ranging from system resource to application stack traces. Strong SQL, data modelling and data analysis skills are a must. Hands-on experience in big data engineering jobs using Python, PySpark, Linux, and ETL tools like Informatica . Track record in implementing systems using Hive, Impala and Cloudera Data Platform will be preferred. Hands-on experience in DevOps deployment and data virtualization tools like Denodo will be preferred. Understanding/ Hands on experience of reporting or visualization tool like SAP BO and Tableau will be beneficial but not required. Passion for automation, standardization, and best practices. Good written and verbal communication and interpersonal skills, ability to understand the business requirement, communicate confidently with stakeholders. AvePoint is proud to employ talent from many different backgrounds, experiences, and identities. We believe that diversity and inclusion drives our success and is at the core of how we hire, communicate, and collaborate to deliver value and excellence. We are committed to fostering an environment where people can bring their whole selves to work and feel a sense of belonging, and we continue to work toward creating a workforce that represents the diversity of our customers and communities. Any personal data you share with us during the application process will be processed strictly in compliance with applicable data protection laws and our Privacy Notice .

About AvePoint: Securing the Future. AvePoint is a global leader in data management and data governance, and over 21,000 customers worldwide rely on our solutions to modernize the digital workplace across Microsoft, Google, Salesforce and other collaboration environments. AvePoint’s global channel partner program includes over 3,500 managed service providers, value added resellers and systems integrators, with our solutions available in more than 100 cloud marketplaces. To learn more, visit www.avepoint.com . At AvePoint, we are committed to investing in our people. Agility, passion and teamwork set us up to do our best work and foster a culture where you are empowered to craft your career, make an impact, and own (y)our future. Unleash the power of you! About the role: •Support Data Movement project. Project Summary: •The successful candidate will be involved in building, integrating and testing a Data Toolkit to standardize efforts across the organization to align Data Ingestion and Observability. •Executing and supporting system and data migrations to the “Data Toolkit” from Legacy systems and processes. Skillset (Must have) Possess a degree in Computer Science/Information Technology or related fields. Proficient in Python or Java/Kotlin (Must upskill and use Python). Familiarity with OpenSearch, Elasticsearch or Solr. Strong software engineering, analytical and problem-solving skills. Good team player and communication skill. Skillset (Good to have) Good foundation in Data such as SQL, ELT/ETL and Pipelines. AvePoint is proud to employ talent from many different backgrounds, experiences, and identities. We believe that diversity and inclusion drives our success and is at the core of how we hire, communicate, and collaborate to deliver value and excellence. We are committed to fostering an environment where people can bring their whole selves to work and feel a sense of belonging, and we continue to work toward creating a workforce that represents the diversity of our customers and communities. Any personal data you share with us during the application process will be processed strictly in compliance with applicable data protection laws and our Privacy Notice .

Your roles and responsibilities include: Collaborating with business users to understand their key priorities and use cases; proposing and developing solutions using data science and/or Generative AI techniques to drive business value Researching emerging AI/data science techniques and identifying relevant ones for EDB to explore and adopt (e.g. Agentic, LLM, Predictive, Fraud/Anomaly Detection, Text Analytics, Customer Segmentation) Data wrangling & analysis - preprocessing, cleaning and feature engineering Supporting the daily operations and maintenance of deployed data science models and products, including Assistants on PAIR / AIBots Developing backend APIs and services to support AI model deployment and integration Building frontend interfaces and user experiences for AI-powered applications Documenting changes to existing products Reviewing and implementing fixes for reported security vulnerabilities; JOB REQUIREMENTS To perform this role, you must have/ be: Minimum of Bachelor’s Degree in Computer Science, Computer Engineering, Machine Learning / Data Science / AI or related disciplines; Able to understand and apply a range of AI/ML techniques for regression and classification Familiar with popular python packages ( e.g. pandas, matplotlib, scikit-learn, XGBoost, NLTK, spaCy ) Understanding of LLM concepts (e.g. context windows, embeddings, chunking, token management) and architectures (e.g. RAG) Experience with context engineering techniques and prompt optimization strategies Proficient in git, SQL Proficient in Business Intelligence tools (e.g. Tableau, Qlik, MS PowerBI, Microstrategy) Bonus skillsets which will allow you to excel in this role include: Proficient in modern programming languages (e.g. typescript, C#) Experience in cloud platform and services preferably in AWS Experience in Docker/Kubernetes Experience with web frameworks and full-stack development, including backend frameworks (e.g. FastAPI, Flask, Express.js) and RESTful API development, and frontend technologies (e.g. React, Vue.js, HTML/CSS, TypeScript) Experience with LLM frameworks (e.g. LangChain, LlamaIndex, Hugging Face Transformers) Knowledge of vector databases and embedding techniques (e.g. Pinecone, Chroma, FAISS) Understanding of AI agent frameworks and multi-agent systems Strong presentation skills and ability to explain technical concepts clearly to a non-technical audience Proficient in statistical software tools (e.g. R, SAS) Any personal data you share with us during the application process will be processed strictly in compliance with applicable data protection laws and our Privacy Notice .

Securing the Future with AvePoint AvePoint is a global leader in data management and governance, trusted by over 21,000 customers worldwide to enhance their digital workplaces across Microsoft, Google, Salesforce, and other collaboration platforms. Our global channel partner program includes more than 3,500 managed service providers, value-added resellers, and systems integrators, with our solutions featured in over 100 cloud marketplaces. To learn more, visit www.avepoint.com . At AvePoint, we are dedicated to investing in our people. Our culture, driven by agility, passion, and teamwork, empowers you to shape your career, make a significant impact, and take ownership of your future. Discover how you can unleash your potential with us! Job Summary: We are seeking a skilled DevOps Engineer to join our DevSecOps team. In this role, you will manage our infrastructure and work closely with application teams to automate software deployments. You will also take part in projects aimed at enhancing, optimizing, and transforming our infrastructure for better efficiency. Key Responsibilities: Develop and automate CI/CD pipelines using tools such as Jenkins, CloudBees, and Bitbucket. Maintain and support full-stack DevOps toolsets, including Jira, Confluence, Bitbucket, CloudBees, Jenkins, SonarQube, Nexus IQ, and Fortify. Automate manual tasks through coding, scripting, and innovative solutions to reduce repetitive human work. Leverage APIs for DevOps tools to build enhanced automation capabilities. Troubleshoot and perform root cause analysis (RCA) on DevOps platform issues. Support and maintain DevOps toolsets running on AWS, container clusters, and Kubernetes. What We Are Looking For: Extensive experience as a DevOps or DevSecOps practitioner in a production environment. Strong technical background in Unix/Linux/PowerShell scripting. Experience with installing, configuring, integrating, upgrading, and patching CI/CD toolsets. Familiarity with customizing Jira and setting up Confluence spaces. Passionate about cloud infrastructure, automation, and CI/CD pipelines. Ability to quickly learn and adapt to new technologies. Strong knowledge of Linux Systems Administration and networking fundamentals (IPv4, TCP, UDP, DNS, HTTP, TLS, routing, load-balancing). Proficiency with AWS (VPC, EC2, IAM, S3, EKS, EFS). Familiarity with ELK, Grafana, Ansible, Terraform, Docker, containers, and Kubernetes. AvePoint is proud to employ talent from many different backgrounds, experiences, and identities. We believe that diversity and inclusion drives our success and is at the core of how we hire, communicate, and collaborate to deliver value and excellence. We are committed to fostering an environment where people can bring their whole selves to work and feel a sense of belonging, and we continue to work toward creating a workforce that represents the diversity of our customers and communities. Any personal data you share with us during the application process will be processed strictly in compliance with applicable data protection laws and our Privacy Notice . Any personal data you share with us during the application process will be processed strictly in compliance with applicable data protection laws and our Privacy Notice .

DevSecOps Engineer (Contractor role, GovTech project) The Government Digital Product (GDP) Team aims to spearhead the digital transformation of government. GDP was established to develop new capabilities focusing on strategic systems of engagement where ICT provides a differentiating factor to citizens. The team functions to deliver digital information and transactional services leveraging on Agile Application Development, Analytics, User Experience Design, Design Thinking and Web Application Performance Optimisation. Who We Are Looking For We are looking for a talented and motivated DevOps Engineer to join our dynamic product development team. You will play a key role in building, automating, securing, and maintaining the engineering platforms and cloud infrastructure that support our software products. You will work closely with software engineers, product teams, and stakeholders to ensure our systems are reliable, scalable, secure, and efficient. Be part of a cross-functional team that values quality, automation, operational excellence, and user experience in the software we deliver. Responsibilities Design, build, and maintain cloud infrastructure and deployment pipelines to support software delivery across web and mobile products. Automate infrastructure provisioning, configuration, and operations using Infrastructure as Code and scripting. Implement and manage CI/CD pipelines to improve deployment speed, consistency, and reliability. Monitor system health, performance, and availability, and proactively identify and resolve issues. Strengthen platform security by implementing best practices for access control, secrets management, vulnerability management, and secure deployment. Support incident response, troubleshooting, root cause analysis, and continuous improvement of system resilience. Collaborate with software engineers, product managers, and other stakeholders to enable efficient development workflows and reliable releases. Review system architecture and operational processes to improve scalability, maintainability, and cost efficiency. Establish and maintain operational documentation, runbooks, and technical standards aligned with architectural and governance guidelines. Drive adoption of DevOps, observability, and reliability best practices across the team. Stay updated with the latest industry trends, tools, and practices in cloud infrastructure, platform engineering, DevOps, and site reliability. Key Experiences and Quali fi cations We Seek: Educational Background : Bachelor’s degree or higher in Computer Science, Information Systems, Engineering, or a related field. Professional Experience : 5+ years of relevant experience in DevOps, Platform Engineering, Site Reliability Engineering, or Infrastructure Engineering roles. 3+ years of experience leading or guiding small engineering teams, projects, or technical initiatives. Technical Expertise : Strong hands-on experience with cloud infrastructure and services, preferably on AWS, including services such as ECS Fargate, Lambda, S3, Aurora, RDS, IAM, CloudWatch, and networking/security components. Proficiency with containerization and orchestration concepts, such as Docker and container-based deployments. Experience building and maintaining CI/CD pipelines and release automation workflows on GitLab and Jira. Strong understanding of Infrastructure as Code and configuration management practices such as Terraform and Cloudformation. Good knowledge of system reliability, observability, monitoring, alerting, and logging practices. Experience supporting modern application stacks, with sufficient understanding of full-stack systems such as React, Node.js, and React Native to enable effective collaboration with development teams. Familiarity with database operations and administration concepts for MySQL and PostgreSQL. Strong understanding of authentication, middleware, application security, API integrations, and system connectivity. Solid grounding in computer science and engineering fundamentals, including cloud computing principles, software design patterns, and secure system design. Problem-Solving Skills: Ability to break down complex infrastructure and operational problems into manageable, actionable items. Proven ability to formulate structured, practical solutions that improve delivery, reliability, and operational outcomes. Additional Skills (Bonus Points) : Experience working with Singapore whole-of-government systems or regulated environments. Proven ability in stakeholder management and cross-team coordination. Knowledge of disaster recovery, backup strategies, business continuity planning, and operational readiness. Experience with platform governance, compliance, and production support processes. Character traits we look out for: Team player Drive for learning and self-discovery Openness to new ideas Strong sense of ownership and proactiveness in ‘making things happen’ Willingness and capability to share and impart knowledge Good verbal and written communications skills and ability to handle engagements internally and externally Any personal data you share with us during the application process will be processed strictly in compliance with applicable data protection laws and our Privacy Notice .

We are looking for a highly skilled AI Engineer specializing in Large Language Models (LLMs) and Agentic AI. You will architect, build, and deploy production-grade LLM applications — from intelligent knowledge bases and RAG systems to autonomous multi-agent workflows. You will work hands-on with open-source Chinese and international LLMs (DeepSeek, Qwen, Kimi, etc), implementing everything from model deployment and inference optimization to prompt engineering and agent orchestration. This is a builder role for someone who thrives at the intersection of research and engineering. KEY RESPONSIBILITIES LLM Application Development Design and develop enterprise LLM-powered applications: intelligent Q&A systems, enterprise knowledge base assistants, AI copilots, document analysis tools, and automated customer service agents. Architect and implement end-to-end RAG (Retrieval-Augmented Generation) systems: document parsing and chunking (recursive, semantic, agentic), embedding generation (BGE, M3E, GTE), vector retrieval (dense + sparse hybrid search), reranking (bge-reranker, Cohere Rerank), and response synthesis with source attribution. Develop and optimize Prompt Engineering strategies: chain-of-thought, tree-of-thought, few-shot prompting, structured output parsing (JSON mode / Pydantic), prompt templates (LangChain/LangSmith), and prompt version management. Knowledge in harness engineering, context management in ensuring LLM interactions and or AI agents reliable and deterministic. AI Agent & Multi-Agent Systems Design and build AI Agent systems using ReAct, Plan-and-Execute, Reflection, and multi-agent collaboration patterns. Implement Function Calling and tool-use capabilities, enabling agents to interact with external APIs, databases, and enterprise systems. Develop multi-agent orchestration using LangGraph, AutoGen, CrewAI, and other agent frameworks to solve complex enterprise tasks through agent collaboration. Design MCP (Model Context Protocol) integrations for standardized LLM tool interoperability. Open-Source LLM Deployment & Optimization Deploy and optimize latest version of open-source Chinese LLMs: DeepSeek, Qwen, and Kimi for on-premise and private cloud environments. Implement model inference optimization: quantization (GGUF/llama.cpp, GPTQ, AWQ, AutoAWQ, FP8/INT8), KV Cache optimization, continuous batching (vLLM, TensorRT-LLM, TGI, SGLang), speculative decoding, and tensor parallelism for high-throughput serving. Build and maintain model serving infrastructure using vLLM, TensorRT-LLM, Text Generation Inference (TGI), Ollama, Xinference, and SGLang; configure GPU resource scheduling with Kubernetes + GPU operators. AI gateway tools for routing, model tracking and load balancing such as TrueFoundry, Kubeflow, LiteLLM or Ray for heavy deep learning. Model Fine-Tuning & Customization Implement efficient fine-tuning pipelines using LoRA, QLoRA, DoRA, and full-parameter fine-tuning on proprietary domain-specific datasets. Prepare and curate instruction-following datasets, RLHF/RLAIF datasets, and evaluation benchmarks for domain adaptation. Evaluate fine-tuned models using automated benchmarks and LLM-as-a-Judge methodologies. Evaluation & Production Operations Build and maintain LLM evaluation frameworks: LLM-as-a-Judge, RAGAS, DeepEval, ARES, and custom task-specific metrics for continuous quality monitoring. Implement production monitoring for LLM systems: output quality tracking, latency/throughput metrics, cost monitoring, drift detection, and guardrail compliance. Design A/B testing frameworks for model comparison and prompt iteration. Implement LLM security guardrails: input/output filtering, PII detection, prompt injection defense, content moderation, and safety alignment. Research & Technical Leadership Track frontier AI research and evaluate emerging technologies (new model architectures, training techniques, inference methods) for enterprise adoption. Contribute to internal knowledge sharing: tech talks, documentation, and best-practice guides on LLM development. REQUIRED QUALIFICATIONS Bachelor's degree or above in Computer Science, Artificial Intelligence, Machine Learning, or related technical field. Master's or PhD in AI/ML preferred. 2+ years of professional experience in AI/ML engineering with demonstrated production deployment of LLM-based systems at scale. Deep understanding of Transformer architecture, attention mechanisms (MHA, GQA, MQA), and LLM pre-training / fine-tuning / inference paradigms. Expert proficiency in LLM application frameworks: LangChain, LlamaIndex, Haystack, or equivalent production-grade tools. Hands-on experience with RAG system development: vector databases (Milvus, ChromaDB, Qdrant, Weaviate, Pinecone, pgvector), embedding models (BGE, M3E, GTE, OpenAI, Cohere), reranking (bge-reranker, Cohere Rerank, cross-encoders), and advanced retrieval techniques (hybrid search, query expansion, HyDE). Practical experience deploying and tuning open-source Chinese LLMs: DeepSeek, Qwen, Kimi , or international models (Llama 3.x, Mistral, Mixtral, Gemma, Phi). Strong experience with model deployment and serving infrastructure: vLLM, TensorRT-LLM, TGI, Ollama, Xinference, SGLang; GPU resource scheduling (Kubernetes + GPU operators). Proficiency in model quantization and inference optimization: GGUF (llama.cpp), GPTQ, AWQ, AutoAWQ, FP8/INT8; knowledge of KV Cache optimization and memory-efficient attention (FlashAttention, FlashInfer, PageAttention). Solid programming skills in Python; experience with PyTorch, TensorFlow, or JAX; familiarity with FastAPI/Flask for building LLM API services. Experience with LLM evaluation methodologies, A/B testing frameworks, and production monitoring of AI systems. PREFERRED QUALIFICATIONS Experience with agent frameworks: LangGraph, AutoGen, CrewAI, OpenAI Assistants API, and multi-agent orchestration patterns. Familiarity with MCP (Model Context Protocol), OpenAI API specification, and multi-modal LLM capabilities (vision, audio). Experience with prompt optimization tools: DSPy, PromptLayer, LangSmith for systematic prompt engineering. Knowledge of model distillation and efficient transfer learning from large teacher models to smaller student models. Contributions to open-source AI projects or publications in NLP/LLM research venues. Experience with cloud GPU providers and cost optimization for LLM inference at scale. Access to high-performance GPU computing resources for model development and experimentation. Any personal data you share with us during the application process will be processed strictly in compliance with applicable data protection laws and our Privacy Notice .