Careers at Syngentagroup

Position Summary: Lead DS and ML predictive analytics solutions, owning technical strategy and delivery for data science projects of varying complexity. Serve as technical lead for data science squads — driving architecture decisions, code quality standards, and delivery excellence across the team. Bridge business and technical teams by translating complex analytical findings into clear, actionable insights that influence strategic decisions. Foster a culture of continuous learning by mentoring data scientists, promoting best practices in MLOps, and championing innovation in analytical methods Accountabilities: Model Development & Analytics Leadership Build and deploy predictive models and analytics capabilities, leading technical workstreams independently Perform advanced data analysis and EDA to develop fact-based insights and strategic recommendations Lead integration of traditional and nontraditional data sources into production analytical workflows Drive integration of structured and unstructured data sources into robust, scalable analytical pipelines. Platform and MLOps Architect and maintain ML infrastructure using cloud platforms (Databricks, AWS, Azure) and orchestration tools. Implement CI/CD practices for model deployment, versioning, and automated retraining pipelines. Establish model monitoring, drift detection, and performance tracking frameworks in production. Promote reusable components, shared libraries, and standardised development environments across the team. Leadership & Stakeholder Management Act as technical point of contact for stakeholders — translating business problems into clear data science problem statements. Lead cross-functional working groups with business owners, data engineers, and domain experts. Mentor and develop junior and mid-level data scientists through code reviews, knowledge sessions, and project coaching. Facilitate agile ceremonies (sprint planning, retrospectives, technical demos) and hold the team accountable to delivery commitments. Strategic Contribution Identify and prioritise high-impact AI/ML opportunities across Commercial domains (Marketing, Sales, Pricing). Build and share domain expertise, contributing to the development of team capability roadmaps. Influence tooling and platform strategy decisions based on emerging best practices and business requirements. Drive measurable business outcomes — framing model outputs in terms of ROI, efficiency gains, or revenue impact. Bachelor's degree in Computer Science, Mathematics, Statistics, Data Science, or related quantitative field (Master's preferred) 3-5 years professional experience delivering production data science solutions Proven track record leading analytical projects with measurable business impact Advanced proficiency with Python ecosystem (scikit-learn, TensorFlow/PyTorch, pandas, numpy) Strong SQL skills and experience with cloud data platforms (AWS, Azure, Databricks) Experience with ML frameworks, version control (Git), and CI/CD for model deployment Demonstrated leadership through mentoring, technical presentations, or cross-functional project ownership Syngenta is an Equal Opportunity Employer and does not discriminate in recruitment, hiring, training, promotion or any other employment practices for reasons of race, color, religion, gender, national origin, age, sexual orientation, marital or veteran status, disability, or any other legally protected status. #LI-Hybrid

We are seeking an innovative and highly motivated Senior Cloud Engineer / Technical Lead based in Durham, NC, to provide hands-on engineering leadership across cloud platforms, data enablement, infrastructure automation, observability, high-performance computing, and laboratory modernization. In this role, you will design and deliver secure, scalable, and supportable capabilities across AWS, Snowflake, Terraform Cloud, Datadog, SageMaker, AWS ParallelCluster, and hybrid infrastructure. This opportunity is ideal for a senior engineer with 4–6+ years of relevant technical experience who can execute strategy autonomously, lead technical workstreams, solve complex problems, contribute to new solutions, and influence outcomes across A&DS / RDIT and at least one additional global or regional function. Accountabilities: As the Senior Cloud Engineer / Technical Lead, you will: Cloud & Hybrid Infrastructure: Design, engineer, and support secure, resilient, and scalable AWS and hybrid-infrastructure solutions across compute, storage, networking, identity, security, databases, and container platforms. Infrastructure Automation: Lead infrastructure automation through Terraform Cloud, including reusable modules, workspace patterns, policy controls, CI/CD integration, and migration from manually managed environments. Data Platform Enablement: Enable and support Snowflake workloads by designing integrations for networking, identity, data movement, monitoring, governance, and operational readiness. Observability & Reliability: Establish observability and reliability practices using Datadog and AWS monitoring, including dashboards, service indicators, alerting, logging, performance analysis, and incident-response improvements. HPC, AI/ML & Research Computing: Deliver technical solutions for high-performance computing, AI/ML, and research-computing needs, including AWS ParallelCluster, Slurm, FSx, SageMaker, scientific pipelines, and scalable workload patterns. Laboratory Modernization : Lead technical workstreams supporting laboratory modernization by translating laboratory device and workflow needs into secure cloud, network, storage, security, and support designs. Technical Leadership: Provide technical leadership through architecture reviews, mentoring, design guidance, risk and dependency management, and resolution of complex incidents, reliability issues, and cost-optimization opportunities. Cross-Functional Partnership: Partner with engineering, research, data, cybersecurity, network, laboratory, vendor, and leadership stakeholders to communicate technical recommendations, drive adoption of standards, and deliver operationally supportable solutions. Required: Bachelor’s degree or equivalent experience in a relevant technical discipline. 5+ years of relevant technical experience in cloud engineering, infrastructure engineering, DevOps, platform engineering, systems engineering, or a related technical field. Advanced hands-on AWS and Terraform Cloud engineering experience. Advanced hands-on Python coding experience. Experience supporting Snowflake, Datadog, hybrid infrastructure, and container platforms. Strong knowledge of cloud networking, IAM, security, storage, databases, and operational resilience. Demonstrated ability to lead technical workstreams, troubleshoot complex problems, and communicate effectively with senior stakeholders. Preferred: Experience with AWS ParallelCluster, Slurm, SageMaker, GPU workloads, or scientific computing. Experience in laboratory, research, life sciences, or high-volume data environments. Knowledge of FinOps, service management, disaster recovery, security governance, and vendor engagement. Experience with hybrid connectivity between on-premises environments and AWS. Relevant AWS, HashiCorp, Snowflake, Datadog, Kubernetes, or Linux certification Candidates must reside in and be permanently authorized to work in the United States. What We Offer: A culture that celebrates belonging and collaboration, promotes professional development, and strives for a work-life balance that supports the team members. Offers flexible work options to support your work and personal needs. Full Benefit Package (Medical, Dental & Vision) that starts on your first day. 401k plan with company match, Profit Sharing & Retirement Savings Contribution. Paid Vacation, Paid Holidays, Maternity and Paternity Leave, Education Assistance, Wellness Programs, Corporate Discounts, among other benefits. Syngenta has been ranked as a top employer by Science Journal. Learn more about our team and our mission here: https://www.youtube.com/watch?v=OVCN_51GbNI Syngenta is an Equal Opportunity Employer and does not discriminate in recruitment, hiring, training, promotion, or any other employment practices for reasons of race, color, religion, gender, national origin, age, sexual orientation, marital or veteran status, disability, or any other legally protected status. WL 5B #LI-HYBRID #LI-LM2

We are looking for a hands-on, senior-level Software Engineer with deep Python expertise to build reliable internal platforms and developer-enablement capabilities. This is an engineering-first role: strong software design, coding fluency and practical problem-solving are essential. You will apply AI in pragmatic ways-such as designing document search and conversational experiences using Retrieval-Augmented Generation (RAG)-while maintaining a high bar for engineering quality, security, scalability and operational excellence. What You’ll Do Design, build and maintain production-grade Python services, libraries and developer tools. Apply modern Python practices, including packaging, type hints, asynchronous programming and maintainable API design. Create shared libraries, service templates and engineering standards that enable multiple internal teams to build consistently and efficiently. Build and evolve AI-enabled capabilities, including RAG-based search and conversational solutions over internal documentation and knowledge sources. Define robust evaluation approaches for AI-assisted features, balancing usefulness, reliability, security and cost. Develop and maintain CI/CD pipelines and cloud-native delivery practices using Docker, Kubernetes and Terraform. Establish strong testing, code quality, documentation-as-code and secure software development practices. Improve application performance, scalability and reliability through profiling, benchmarking, observability and architectural improvements. Implement monitoring, alerting and operational standards for platform services. Conduct thorough code reviews, mentor engineers and raise engineering standards across the team. Partner with engineering, product and scientific stakeholders to translate complex needs into practical, scalable technical solutions. Required Qualifications Significant hands-on experience as a Software Engineer, with strong, production-level proficiency in Python. Equivalent depth in Java may also be considered. Demonstrated ability to write clean, maintainable and well-tested code, and to solve complex engineering problems independently. Strong experience with modern Python practices: packaging, dependency management, typing, async programming and service/API development. Experience designing and maintaining reusable libraries, service templates or internal developer platforms used by multiple teams. Strong knowledge of software quality practices, including automated testing, code reviews, CI/CD and release engineering. Practical experience with Docker, Kubernetes and Infrastructure as Code, ideally Terraform. Experience with observability, monitoring, alerting, performance profiling and benchmarking. Solid understanding of secure coding practices, vulnerability management and security considerations in software delivery. Applied understanding of Generative AI and LLM patterns, particularly RAG. You should be able to explain and design an approach for document search or conversational interfaces over enterprise content. Strong written and spoken English, with the ability to communicate effectively across technical and non-technical stakeholders. Preferred Qualifications Experience building internal platforms, developer tools or products adopted by several engineering teams. Experience in agriculture, life sciences, scientific computing or another regulated enterprise environment. Experience working in distributed, international engineering organisations. Track record of technical leadership, mentoring or leading cross-team engineering initiatives. Familiarity with AI/ML frameworks and tools used in software development environments. What Success Looks Like You are an engineer first: someone who can go deep in code, make sound technical decisions and deliver durable software in production. You use AI thoughtfully and practically-not as a substitute for engineering fundamentals, but as a capability embedded in reliable, secure and scalable platforms. Why Syngenta? • Meaningful impact: help build technology that supports scientific innovation and sustainable agriculture. • Complex, modern challenges: work on high-scale platforms and systems with room for technical judgement. • Collaborative environment: partner with global engineering, R&D and product communities. • Growth: broaden your influence through challenging work, visible outcomes and a global network

The AI Security Platform Lead acts as a vital member of Syngenta’s Information Security team to help safeguard Syngenta Information Technology infrastructure against AI-related cyber threats and vulnerabilities. The AI Security Platform Lead bridges end users, developers, operational run/support teams and other functional stakeholders to maintain reliable operation of AI Security Platform(s) while driving continuous and sustainable improvement and providing regular transparent performance & security metric reporting Accountabilities: Cross-Platform AI Policy Enforcement: Manage and optimize security controls across endpoints, SaaS environments, and run-time interactions to monitor AI agent behaviors and protect against data exfiltration. AI Asset Discovery & Posture Management :Leverage the AI Security Platform to maintain a comprehensive inventory of private and public AI tools while ensuring continuous visibility and security posture across AWS, Azure, GCP. AI Red Teaming : Work with Syngenta application development teams to maintain CI/CD integration of automated AI security Red-Teaming tests that assess internally developed AI agents for security and AI use-policy compliance. Vendor Management & Roadmap : Act as the primary technical point of contact for the AI Security Platform vendor, influencing their product roadmap to meet Syngenta’s evolving AI security needs and staying ahead of new feature releases. Scalability & Performance Tuning : Ensure the AI Security Platform(s) AI security architecture scales effectively with enterprise growth, minimizing latency for end-users while maintaining rigorous inspection standards. Incident Response Integration : Partner with the SOC (Security Operations Center) to develop playbooks for responding to AI-specific threats detected by the AI Security Platform(s). Collaboration and Communication: Collaborate closely across functional, regional and business unit teams and stakeholders to proactively identify, recommend and develop continuous improvements for AI cybersecurity tooling, event detection, correlation and incident response. Documentation and Knowledge Management: Maintain comprehensive documentation of the AI Security Platform, operational procedures, contacts, and security findings for knowledge sharing and audit purposes. Experience managing a large-scale enterprise security solution including capability road-mapping and operational change management Strong understanding of generative AI concepts such as LLMs, RAG, MCP servers, AI agent frameworks, agent loops, agent Red-Teaming, model routing, model gateways, guardrails technologies, LLM context windows, private AI vs public AI and more Familiarity with EU AI Act, OWASP LLM Top 10 and other emerging AI regulatory and security frameworks Hands-on experience with cloud-native security tools and API-based integrations with cloud, network, security SIEM, firewalls, intrusion detection systems, endpoint security, application development IDEs and agentic coding tools Experience with Data Loss Prevention (DLP) specifically tailored for LLMs and generative AI prompts. Excellent analytical, problem-solving, and troubleshooting skills Strong communication and interpersonal skills Ability to work independently and as part of a team Note: Syngenta is an Equal Opportunity Employer and does not discriminate in recruitment, hiring, training, promotion or any other employment practices for reasons of race, color, religion, gender, national origin, age, sexual orientation, gender identity, marital or veteran status, disability, or any other legally protected status. Follow us on: Twitter & LinkedIn https://twitter.com/SyngentaAPAC https://www.linkedin.com/company/syngenta/ India page https://www.linkedin.com/company/70489427/admin/

Role purpose The Data Platform Engineer Manager is responsible for designing, building, operating, and continuously improving enterprise‑scale , Databricks based data mesh platform at Syngenta. This role focuses on platform reliability, scalability, security, and enablement of data engineers, analytics, and data science teams , enable domain autonomy to ingest, process, and share data using Databricks , while ensuring security, quality, and interoperability. Knowledge, experience & capabilities Databricks Ecosystem: Expert-level knowledge of Databricks Lakehouse, Spark SQL, Delta Lake, Unity Catalog etc. Programming: Proficiency in Python, PySpark, or Scala for ETL/ELT development. Cloud Platforms: Hands-on experience with at least one major cloud provider (Azure, AWS, or GCP) and related data services. AWS Preferable. DevOps & Infrastructure-as-Code (IaC): Familiarity with Terraform, CI/CD pipelines, and DevOps practices. Data Governance: Experience with implementing data lineage, PII anonymization, and data quality frameworks. Critical success factors & key challenges Strong data engineering skills , logical and reasoning Ability to deliver POCs, MVPs, Experiments, technology evaluations following design thinking practices Ability to orchestrate efforts needed to prioritize business initiatives across complex change agendas Excellent communication and stakeholder management skills to explain technical information to individuals who don't have the same technical background Problem solving and decision making skills Teamwork, team management and leadership skills Qualification & Experience Level B.tech/M.tech 5+ years of experience in data engineering, data platform engineering, or architecture. 3+ years of hands-on experience specifically with Databricks in production environments. Certifications: Databricks Certified Professional Innovations Employee may, as part of his/her role and maybe through multifunctional teams, participate in the creation and design of innovative solutions. In this context, Employee may contribute to inventions, designs, other work product, including know-how, copyrights, software, innovations, solutions, and other intellectual assets.