AI Automation Engineer

AI Automation EngineerChicago, Illinois, United Statesfairlife, LLC is a Chicago-based nutrition company that creates great-tasting, nutrition-rich and dairy products to nourish consumers.With over $3B in annual retail sales, fairlife's portfolio of delicious, lactose-free, real dairy products includes: fairlife® ultra-filtered milk; Core Power® High Protein Shakes, a sports nutrition drink to support post-workout recovery; fairlife® nutrition plan™, a nutrition shake to support the journey to better health.A wholly owned subsidiary of The Coca-Cola company, fairlife, LLC has been recognized by both Fast Company and Nielsen for its industry leading innovation.The AI Automation Engineer will play a key role in advancing fairlife's vision of establishing a world-class Decision Intelligence ecosystem. This role centers on building AI agents and automated workflows that reason, plan, and execute tasks end-to-end — turning manual, repetitive processes into reliable automation across manufacturing, supply chain, and business operations. Working in a modern Microsoft Fabric and Azure environment, the engineer will design, develop, and deploy production-grade agentic systems that connect to enterprise tools and data to unlock operational efficiency and faster, better-informed decisions.Responsibilities:Design, build, and deploy AI agents that autonomously reason, plan, and execute multi-step tasks to automate business workflowsIdentify manual, repetitive, and high-friction processes and re-engineer them as reliable, automated agentic workflowsOrchestrate single- and multi-agent workflows using frameworks such as LangChain, LangGraph, CrewAI, or AutoGenIntegrate agents with internal tools, APIs, databases, and enterprise systems — including via the Model Context Protocol (MCP) — to enable reliable, end-to-end task executionImplement retrieval-augmented generation (RAG) pipelines to ground agents in fairlife's domain-specific dataApply prompt engineering, tool and function calling, and rigorous evaluation to make agents accurate, reliable, and production-readyLeverage MLOps/LLMOps best practices to deploy, monitor, evaluate, and continuously improve agentic systems in productionBuild responsible-AI guardrails — safety, human-in-the-loop oversight, transparency, and compliance — into every automated systemPartner with the Senior Data Scientist and cross-functional teams to identify and prioritize high-impact automation opportunitiesSkills/Qualifications Required:2+ years of relevant experience in AI engineering, machine learning engineering, software engineering, or related fieldsBachelor's or Master's degree in Computer Science, Data Science, AI/Machine Learning, Engineering, or a related STEM field (or equivalent practical experience)Proficiency in Python and experience building applications with large language models (LLMs) and foundation modelsHands-on experience with agentic AI frameworks (e.g., LangChain, LangGraph, CrewAI, AutoGen) and orchestration patternsExperience with prompt engineering, tool and function calling (including the Model Context Protocol), retrieval-augmented generation (RAG), and vector databases (e.g., Pinecone, Weaviate, ChromaDB)Familiarity with cloud platforms (Azure/Fabric, AWS, or GCP) for scalable AI deploymentExperience with MLOps/LLMOps practices and responsible AI principles, including monitoring, governance, and complianceStrong software engineering fundamentals, including API development, version control (Git), and containerization (Docker)Strong communication skills for collaborating with business and technical teamsA bias toward automation: a track record of spotting repetitive, manual workflows and building tools or agents to eliminate them (preferred)Experience in manufacturing and supply chain (preferred)How fairlife Nourishes You:At fairlife, we believe in better — and that includes how we support our people. We offer a comprehensive suite of benefits and wellbeing resources designed to support you physically, emotionally, socially, and financially, both in and out of work.Comprehensive medical, dental, and vision coverage, effective day one!Supplemental health plans (hospital indemnity, accident, and critical illness insurance)Paid Time Off to recharge and support work-life balancePaid parental leave & adoption assistance (up to $10,000)Parental support & family care benefits, including childcare resources and lactation support401(k) to support retirement planning with up to 9% in employer matchWellness reimbursement (up to $500 for qualified wellbeing expenses)Employee Assistance Program (EAP) for emotional wellbeing and work-life supportCompany-paid life insurance and short-term disabilityEmployer HSA funding (for HDHP participants)Tuition reimbursement (up to $10,000) and student loan repayment ($200/month)Learning & development programs to unlock your full potentialRewards & recognition, matching gifts, free product, and Business Resource Groupsfairlife's nour!sh program is designed to meet you where you are — supporting your individual wellbeing journey while enabling you to do your best work every day.This position requires current authorization to work in the United States. Visa sponsorship is not available for this position. This is a hybrid position based in our downtown Chicago office, with an expectation of working onsite three (3) days per week.Position location: Chicago, ILReports to: Senior Data Scientist, Decision IntelligenceTravel requirements: 10%Exempt/Nonexempt: ExemptBase pay offered may vary depending on geography, job-related knowledge, skills, and experience. A full range of medical, financial, and/or other benefits, dependent on the position, is offered.Base pay range: $85,000 - $105,000 USDfairlife, LLC is an equal opportunity employer. We do not discriminate on the basis of race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. All qualified applicants and employees will be given equal opportunity. Selection decisions are based on job-related factors.