AI Developer

Job Summary:

Lead the design, build, and ongoing support of AI Assistants, AI Agents, and automation solutions—leveraging ChatGPT based copilots, Copilot-Studio-Onboarding--Building,-Testing,-and-Deploying-AI-Agents-for-Maximum-Value-at-Cargill agents, and Power Automate workflows—while adhering to AI CoE platform standards and guardrails.

Deliverables typically span API and integration services, retrieval, and vector-search pipelines, LLMOps/AgentOps operational tooling, and intuitive front-end experiences for both office users and plant operators.

This role will work primarily within our Food EMEA Digital Technology and Data team and assist with solutions for our technology stack servicing our Food EMEA team and Customers.

Responsibilities

• Develop multi-agent workflow automation patterns using Agentic AI

• Process redesign and mapping to agentic workflow patterns

• Architect scalable micro-services that wrap LLM/RAG/Agent workflows (Python).

• Implement robust prompt-engineering patterns, retrieval pipelines, and caching for AI Assistants and AI Agents

• Profile inference latency, GPU/CPU utilization, and memory

• Writes and maintains highly complex unit tests and integration tests, and performs debugging to maintain the quality and performance of the software/AI solutions

• Lead bug-fix, security-patch, and performance-tuning sprints for live AI Assistants and AI Agents

• Leads the establishment and maintenance of comprehensive documentation for agents, applications, deployment processes, and system configurations.

• Own on-call runbooks, SLOs, and incident reviews; embed observability

• Leads and mentors technical support and troubleshooting for highly complex issues with deployed applications to ensure minimal downtime and fast resolution.

• Leads cross functional team of engineers to gather requirements and deliver solutions that meet business needs.

• Experience working within product and agile delivery frameworks including backlog management and cross-functionality delivery and full E2E engineering.

• Experience with enterprise architecture, data governance, or security standards in the context of digital solution delivery as part of E2E engineering and solution architecture.

• Experience with digital technologies in the commercial & sales management capability (e.g. Salesforce, Service Cloud, CPQ, Salesloft, ChatGPT or similar platforms)

• Experience:

• Minimum requirement of 2 years of building production software or data platforms.

• Typically, 3 years or more with 2+ years of cloud-native AI/ML or GenAI systems (Azure, AWS, or GCP) or 2+ years of software development.