AI Engineer

Company Description

Syngenta is one of the world's leading agriculture innovation company (Part of Syngenta Group) dedicated to improving global food security by enabling millions of farmers to make better use of available resources. Through world class science and innovative crop solutions, our 60,000 people in over 100 countries are working to transform how crops are grown. We are committed to rescuing land from degradation, enhancing biodiversity and revitalizing rural communities.

A diverse workforce and an inclusive workplace environment are enablers of our ambition to be the most collaborative and trusted team in agriculture. Our employees reflect the diversity of our customers, the markets where we operate and the communities which we serve. No matter what your position, you will have a vital role in safely feeding the world and taking care of our planet.

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Job Description

Accountabilities

• AI Solution Architecture & Implementation
• Architect production-ready AI solutions integrating generative models with enterprise systems
• Design intelligent workflows combining generative AI, computer vision, and speech recognition
• Build hybrid architectures merging rule-based logic with LLMs
• Develop scalable Agentic AI implementations using AWS Bedrock and Langgraph platforms
• Create data pipelines, feature stores, and model serving architectures
• Advanced LLM Integrations
• Implement advanced prompt engineering: chain-of-thought, zero-shot/few-shot learning, constitutional AI
• Build sophisticated RAG systems with vector databases
• Design agentic AI using function calling, tool use, and autonomous frameworks (LangChain, LlamaIndex)
• Deploy custom generative applications via Gen AI Platforms and Assistants API
• Develop multi-modal solutions: GPT for (vision), DALL-E (images), Whisper (speech), Codex (code)
• MLOps & Production Deployment
• Deploy high throughput inference systems with auto-scaling on SageMaker Endpoints
• Implement CI/CD pipelines for AI using SageMaker Pipelines and Databricks Workflows

• Build monitoring systems: model drift detection, performance analytics, cost optimization

• Ensure AI security: prompt injection prevention, adversarial robustness, PII protection, Data leakage

• Establish model governance: version control, audit trails, compliance frameworks
• Technical Leadership & Strategy
• Lead AI transformation initiatives with clear KPIs and success metrics
• Mentor engineers and data scientists on AI best practices and architectural patterns
• Research and pilot emerging AI technologies and frameworks
• Develop AI governance frameworks addressing ethics, bias mitigation, and responsible AI
• Create ROI models and business cases for AI investments
• Create, execute and enforce solutions blueprint

Knowledge, experience & capabilities

Required:

• Master's/PhD in Computer Science (AI/ML specialization) or Data Science or related field
• 7+ years enterprise software development, solution engineering, or AI/ML consulting
• 3+ years production experience with Large Language Models and generative AI
• 2+ years hands-on experience with AWS Bedrock (training, deployment, pipelines, feature store)
• 2+ years hands-on experience with Langgraph, LangChain, Bedrock or Agentic AI frameworks
• Proven track record delivering complex AI projects from conception to production

Preferred:

• AWS Certified Solutions Architect Professional or Machine Learning Specialty
• OpenAI API certification or equivalent demonstrated expertise

Agentic AI Technical Expertise -

• Agent Design Patterns: Expertise in architecting multi-agent systems with clear role definitions, communication protocols, and coordination strategies; implementing ReAct (Reasoning + Acting), Plan-and-Execute, and Reflection patterns; and designing stateful agent workflows using LangGraph with checkpointing, error handling, and fallback mechanisms.
• Knowledge & Context Management: Proficient in designing RAG architectures for agent knowledge access, including chunking strategies, embedding selection, retrieval optimization, and context window management; implementing agent memory systems (short-term, long-term, semantic, and episodic memory); and creating knowledge base governance for version control, access policies, and content validation.
• Agent Observability & Governance: Skilled in establishing monitoring and evaluation frameworks for agent performance, accuracy, latency, and cost; implementing responsible AI controls including bias detection, hallucination mitigation, PII protection, and content filtering; and creating feedback loops for continuous agent improvement and human-in-the-loop validation.

Business & Leadership Skills

• Translate complex AI concepts into business value for C-level executives
• Lead cross-functional teams (3-8 engineers, architects)
• Conduct architecture reviews and drive technical decision-making
• Facilitate workshops, design sprints, and requirements gathering
• Build trusted advisor relationships with stakeholders
• Develop business cases with ROI projections and risk assessments.
• 7+ years of hands-on experience in AI/ML engineering or related roles
• Proven track record of deploying models to production environments
• Proficiency in Python
• Excelent understanding of AWS cloud architecture including Sagemaker and Bedrock
• Ability to use identify and re-use GitHub projects to solve business problems
• Experience working with cross-functional teams and translating business needs into technical solutions
• Demonstrated ability to manage multiple projects and deliver results in agile environments

Qualifications

Bachelor's or Master's degree in informatics, computer science, or a related AI field.

Additional Information

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.

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