As a Senior Qualified AI & Data Science professional at Cargill, your role involves planning and leading the development of artificial intelligence models to drive decision-making processes. With minimal supervision, you will extract and integrate complex data from various sources, build advanced models suitable for business use cases, and evaluate model performance for accuracy, scaling, and deployment. Additionally, you will develop compelling and clear communication materials to facilitate partner onboarding.

Key Responsibilities:

• Designing and delivering end-to-end AI solutions from concept to production using Python, integrating LLMs, RAG pipelines, and structured agent workflows.
• Building and operating solutions on a multi-team AI platform, leveraging shared services such as LLM gateways, vector stores, and tool ecosystems.
• Implementing CI/CD pipelines with automated testing, evaluation checks, and safe deployment practices to ensure reliable and repeatable releases.
• Preparing and integrating complex datasets, applying feature engineering, and evaluating model and retrieval performance for accuracy and scalability.
• Developing robust agent behaviors including tool calling, retries, guardrails, and deterministic outputs aligned to business requirements.
• Applying telemetry-first practices (logs, metrics, traces), supporting on-call readiness, and maintaining runbooks to meet defined SLAs/SLOs.
• Implementing secure-by-default practices including secrets management, access control, and protections against prompt injection and data leakage.

Qualifications

• Minimum requirement of 7 years of relevant work experience.

Preferred Skills:

• Experience designing AI solutions using RAG patterns including chunking, embeddings, and retrieval evaluation.
• Familiarity with multi-model orchestration, routing, and cost/latency optimization strategies.
• Experience with AWS services including containers, IAM, networking, and observability tooling.
• Exposure to LLM observability tools (traces, evals, datasets) for debugging and quality monitoring.
• Experience building reusable tool interfaces or MCP-style integrations for agent ecosystems.
• Ability to create solution diagrams, workflows, and technical blueprints with stakeholders.
• Familiarity with infrastructure as code and environment promotion strategies in CI/CD pipelines. As a Senior Qualified AI & Data Science professional at Cargill, your role involves planning and leading the development of artificial intelligence models to drive decision-making processes. With minimal supervision, you will extract and integrate complex data from various sources, build advanced models suitable for business use cases, and evaluate model performance for accuracy, scaling, and deployment. Additionally, you will develop compelling and clear communication materials to facilitate partner onboarding.

Key Responsibilities:

• Designing and delivering end-to-end AI solutions from concept to production using Python, integrating LLMs, RAG pipelines, and structured agent workflows.
• Building and operating solutions on a multi-team AI platform, leveraging shared services such as LLM gateways, vector stores, and tool ecosystems.
• Implementing CI/CD pipelines with automated testing, evaluation checks, and safe deployment practices to ensure reliable and repeatable releases.
• Preparing and integrating complex datasets, applying feature engineering, and evaluating model and retrieval performance for accuracy and scalability.
• Developing robust agent behaviors including tool calling, retries, guardrails, and deterministic outputs aligned to business requirements.
• Applying telemetry-first practices (logs, metrics, traces), supporting on-call readiness, and maintaining runbooks to meet defined SLAs/SLOs.
• Implementing secure-by-default practices including secrets management, access control, and protections against prompt injection and data leakage.

Qualifications

• Minimum requirement of 7 years of relevant work experience.

Preferred Skills:

• Experience designing AI solutions using RAG patterns including chunking, embeddings, and retrieval evaluation.
• Familiarity with multi-model orchestration, routing, and cost/latency optimization strategies.
• Experience with AWS services including containers, IAM, networking, and observability tooling.
• Exposure to LLM observability tools (traces, evals, datasets) for debugging and quality monitoring.
• Experience building reusable tool interfaces or MCP-style integrations for agent ecosystems.
• Ability to create solution diagrams, workflows, and technical blueprints with stakeholders.
• Familiarity with infrastructure as code and environment promotion strategies in CI/CD pipelines.