Applied AI/ML Engineer

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Key Responsibilities

Business Requirement Gathering: Partner with supply chain functional leads to elicit and document business requirements and translate them into technical specifications for AI-driven decision support tools, ensuring every solution delivers measurable business value.

Model Integration & Deployment: Act as the primary technical lead for applied AI implementation. Take pre-developed models from internal partners or 3rd-party vendors (COTS) and successfully deploy them within the supply chain GCP space.

Graph-Based AI Implementation: By working closely with Knowledge Graph engineering teams to execute model interface against enterprise ontologies, you will design decision-intelligence frameworks that proactively identify and mitigate risks across the global N-tier supplier network. By simulating "what-if" scenarios using Generative AI and Graph analytics, you will enable the supply chain to remain resilient against geopolitical, environmental, and logistical shocks, providing automated prescriptive solutions for supply-chain, logistics and capacity re-allocation before disruptions impact production .

AI-Driven SDLC Execution: Champion and implement AI-assisted development practices. Implement agentic workflows (e.g., AutoGen, CrewAI) and Use LLM-based tools (e.g., GitHub Copilot, automated PR agents, and AI-generated documentation) to accelerate delivery with high code quality for the Decision Intelligence platform

Pipeline & MLOps/LLMOps Engineering: Design the "connective tissue" between Knowledge Graph updates and model inference engines. Establish rigorous guardrail frameworks for toxicity, hallucination rates, and latency. Maintain automated pipelines that ensure decision-support tools are always powered by the most current data.

Technical Standardization: Develop reusable integration patterns and data contracts to ensure that AI solutions can be scaled across multiple business units without redundant engineering effort.

Minimum Requirements

Bachelor’s degree in Computer Science, Engineering, Data Science, or a related technical field.

2+ years of progressive experience in AI/ML, data science, or advanced analytics, with a proven track record of delivering production-grade solutions in large enterprise environments.

Strong proficiency in Python and SQL. Familiarity with Graph Query Languages (e.g., Cypher).

Demonstrated experience with MLOps principles and tools (e.g., Azure ML, AWS SageMaker, GCP AI Platform, Kubeflow, MLflow) and designing / implementing AI-specific SDLCs.

Experience in LLMOps capability: Experience designing, deploying, and maintaining LLM-powered applications in production, with focus on prompt engineering, RAG pipelines, safety controls, hallucination mitigation, observability, cost optimization, and continuous testing and evaluation using metrics like accuracy,F1,BLEU, ROUGE etc and frameworks like RAGAS ,DeepEvaletc

Strong technical expertise in cloud services (GCP/Vertex AI) and data integration patterns

Exceptional communication & interpersonal skills, to translate complex AI logic into strategic recommendations for supply chain business leaders.

Preferred Qualifications

AI-SDLC Experience: Proven track record of using AI tools to enhance personal or team productivity (e.g., Agentic workflows, RAG-based requirement synthesis).

Requirement Engineering: Experience in a product engineering role with proven track record of translating business needs into technical specifications for applied AI implementation.

Knowledge Graph: Understanding semantic ontologies and how they enable advanced analytics.

COTS Integration: Experience integrating COTS AI solutions into an enterprise tech stack.

Supply Chain Domain Knowledge: Functional understanding of supply chain operations, including demand & capacity planning, logistics, sustainability & risk management, resilience, etc.

Research to Production: Ability to research and rapidly apply & build a functional prototype that meets Ford’s standards for security and scalability

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