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.
To learn more visit: www.syngenta.com
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.
Follow us on: Twitter & LinkedIn
https://twitter.com/SyngentaAPAC
https://www.linkedin.com/company/syngenta/
India page
https://www.linkedin.com/company/70489427/admin/
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