# Cognizant / Toyota — AI Engineer (Contractor) - Generated: 2026-09-20 07:39:17 PM EDT - Original/canonical posting: https://docs.google.com/document/d/154SRHvJ8RVp7qgyAXzDGMS8fLeItykUWSq82UL2-jUw/edit?tab=t.0#heading=h.4tfu32wnlpdj - Provider: User-provided Google Docs JD - Posting time: Not disclosed - Elapsed since posting: Not available - Applicants: Not disclosed - Work model/location: Local or remote; exact local location is not identified - Compensation: Not disclosed - Travel: Not disclosed - Engagement: Contractor; October 1, 2026 through March 31, 2027, structured as two three-month phases; Phase 2 is contingent on Phase 1 results, leadership approval, and funding - Positioning track: Technical IC - Fit outcome: FAIL — 86% capability fit with a hard compensation/title gate ## Direct-match strengths Production Python; LLM applications; RAG; agentic workflows; MCP and structured tool integration; retrieval and vector search; metadata and citations; evaluation design; correctness, traceability, latency, and cost metrics; Responsible AI; privacy; security; data governance; observability; CI/CD; agile delivery; ML lifecycle support; and reusable project scaffolding. ## Hard or material gaps Annual compensation is undisclosed and the title is AI Engineer, which is outside Keith's Director/VP/Chief missing-compensation exception. The six-month contractor scope and stated 5+ year level also create material level/tenure risk, although the hands-on technical mandate is exceptionally well aligned. Travel is undisclosed and is not a failure reason. ## Evidence map 1. Production-quality Python and collaborative software engineering (weight 3, evidence 3/3) — Direct Python, APIs, testing, CI/CD, code review, documentation, observability, and production-operations evidence. 2. LLM applications, RAG, agent workflows, and structured tool integration (weight 3, evidence 3/3) — Built AssistX with LLMs, RAG, agents, MCP, tools, structured workflows, and human review. 3. Retrieval, embeddings, vector/hybrid search, metadata, and citations (weight 3, evidence 3/3) — Direct PGVector, retrieval, embeddings, knowledge graphs, metadata, source grounding, and governed knowledge evidence. 4. Evaluation, traceability, quality measurement, latency, and cost (weight 3, evidence 3/3) — Direct evaluation datasets, synthetic testing, LLM-as-judge, regression, observability, routing, latency, and token-cost controls. 5. Responsible AI, privacy, security, and data governance (weight 3, evidence 3/3) — Direct regulated AI governance, privacy engineering, security, auditability, bias, drift, and human-oversight evidence. 6. Agile delivery, prototypes, technical decisions, backlogs, and runbooks (weight 2, evidence 3/3) — Direct iterative product delivery, roadmaps, prioritized backlogs, release practices, documentation, monitoring, and incident operations. 7. ML lifecycle and project scaffolding (weight 2, evidence 3/3) — Direct ML platforms, data-science enablement, reusable architecture, developer tooling, repository practices, and production bootstrapping. 8. Qualifying compensation disclosure for a non-Director/VP/Chief title (weight 3, evidence 0/3) — No compensation is disclosed for this AI Engineer contractor role. ## Keyword diagnostic Capability overlap is exceptionally strong across Python, RAG, agents, retrieval, evaluation, governance, traceability, project scaffolding, and production engineering. Keyword overlap is not reported because the hard fit gate prevents resume generation. ## Full normalized job description Cognizant / Toyota - AI Engineer (Contractor) Tags: contract ic 2026q3 jobsearch2026 2026-09-17 Related: * Droisys recruiter Abhay Saini * Cognizant (LinkedIn | website) AI Engineer (Contractor) Who We’re Looking For Toyota’s Data Science and Machine Learning (DSML) Team is seeking an experienced AI Engineer to design, build, evaluate, and operationalize the technical foundation of the DS/ML AI Accelerator. This contractor role combines hands-on generative AI engineering, retrieval-augmented generation (RAG), agent workflow design, and software engineering with a strong focus on governance, traceability, and measurable delivery impact. The successful candidate will help DS/ML teams move from fragmented discovery and manual project setup to a reusable, governed acceleration capability. The engagement is structured in two phases, with a formal continuation decision at the end of Phase 1. What You’ll Be Doing Governed Knowledge and RAG * Partner with data and business owners to onboard approved documents, data definitions, prior work, and expert knowledge into a governed knowledge base. * Implement retrieval pipelines, metadata, taxonomy, tagging, source citations, and quality checks that make context discoverable and trustworthy. * Create evaluation datasets and retrieval-quality metrics; diagnose relevance, grounding, completeness, and source-stewardship gaps. * Ensure generated outputs clearly distinguish retrieved facts, inferences, assumptions, and items that need subject-matter-expert confirmation. Agentic Product Engineering * Build the Context Agent, which retrieves and organizes approved DS/ML context into structured, usable outputs for data scientists and stakeholders. * Build the DS/ML Use-Case Agent, which translates business objectives, available data, and prior-project knowledge into structured data-science and machine-learning use cases, including requirements, assumptions, risks, and recommended next steps. * Design multi-step agent workflows that retrieve approved context, use tools safely, produce structured outputs, and retain human review points. * Develop the DS/ML Project Scaffolding Agent, which converts a use case into a standardized project starting point, generating the repository structure, starter code, documentation, templates, and workflow guidance needed to begin delivery efficiently. * Implement prompt, workflow, and tool-orchestration patterns that improve repeatability, traceability, and usability for Data Scientists, ML Engineers, Business Analysts, Product Owners, and SMEs. Evaluation, Delivery, and Governance * Define and instrument technical and user-centered evaluation for agent outputs, including correctness, completeness, traceability, revision effort, latency, and cost-to-serve. * Work in two-week agile sprints; demonstrate working increments, document technical decisions, and convert pilot feedback into a prioritized backlog. * Apply secure development practices and meet Responsible AI, security, privacy, data-governance, and enterprise review-board requirements. * Partner with the future operating owner to define maintainable code, runbooks, monitoring, knowledge-source refresh practices, and an enhancement backlog. What You Bring * Bachelor's degree in Computer Science, Data Science, Machine Learning, or a related field, or equivalent practical experience. * 5+ years of software, data science, machine learning, or AI engineering experience, including production-quality Python development and collaborative version-controlled delivery. * Hands-on experience building LLM-enabled applications, RAG systems, agentic workflows, or comparable AI assistants using APIs and structured tool integrations. * Strong Python skills and experience with modern software engineering practices: * testing, * code review, * CI/CD, * API design, * documentation, and * observability. * Experience with data retrieval/indexing concepts, embeddings, vector or hybrid search, evaluation design, and quality measurement. * Ability to turn ambiguous business problems into testable technical requirements and communicate tradeoffs to technical and nontechnical partners. * Demonstrated commitment to responsible AI, privacy, security, and data-governance practices. Preferred Qualifications * Experience with modern LLM orchestration, agent-workflow, or developer-assistance platforms. * Experience designing LLM evaluation frameworks, guardrails, prompt/version management, telemetry, or model-risk controls. * Experience supporting ML lifecycle workflows, feature or data documentation, model development, or ML project bootstrapping. * Familiarity with data governance, responsible AI, access control, auditability, and human-in-the-loop review. Engagement Structure and Deliverables Phase 1: Foundation, Prototype, and Continuation Decision October 1, 2026 – December 31, 2026 The contractor will establish the core technical foundation for the DS/ML AI Accelerator and deliver the capabilities needed to support a leadership decision on whether to continue and expand the effort. * Current-state assessment of DS/ML context-discovery pain points and productivity opportunities. * Context Agent solution architecture, including approved data sources, retrieval design, context schema, metadata strategy, integrations, guardrails, and operating model. * Prototype Context Agent capable of retrieving and citing relevant information about data usage, processing, business goals, codebases, documentation, and decision history. * Prototype DS/ML Use-Case Agent that translates business objectives, available data, and prior-project knowledge into structured use cases, including requirements, assumptions, risks, and recommended next steps. * Prototype DS/ML Project Scaffolding Agent that produces a standardized repository structure, starter code, documentation, templates, and workflow guidance for approved use cases. * Business-to-ML context template that converts business objectives into structured modeling context. * Documentation of prompts, agent instructions, source-ingestion methods, permission assumptions, limitations, evaluation approach, and recommended operating practices. * Evaluation baseline and evidence of performance across correctness, completeness, traceability, user revision effort, latency, and cost-to-serve. * Phase 2 recommendation, including outcomes, risks, lessons learned, prioritized opportunities, and proposed scope. The CUDS use-case proof of concept is excluded from Phase 1 and will be considered only if the effort proceeds to Phase 2. Phase 2: Expanded Capability Delivery January 1, 2027 – March 31, 2027 Phase 2 is contingent on Phase 1 results, leadership approval, and available funding. If approved, the contractor will build on the Phase 1 foundation to deliver prioritized additional capabilities. The initial priority is the CUDS use-case proof of concept, demonstrating an integrated Context, Use-Case, and Project Scaffolding Agent workflow to retrieve and apply relevant codebases, meeting summaries, data context, and prior analysis within a real DS/ML workflow. Subject to priorities and available capacity, Phase 2 may also include additional agent-enabled capabilities that improve DS/ML efficiency, such as data discovery, feature engineering, model experiment, or model validation agents. Engagement Details Duration: October 1st, 2026 – March 31st, 2027, structured as two three-month phases Location: Local or remote Reporting Structure: Reports to Data Science leadership; collaborates cross- functionally with data and platform engineering, security and domain teams ## Artifact metadata - Resume: Not generated because the fit gate returned FAIL. - Cover letter: Not generated because the fit gate returned FAIL. - LinkedIn note: Not generated for a FAIL role. - Archived JD capture: https://files.keithsteward.com/Cognizant_Toyota/Keith_Steward_AI_Engineer_Contractor_Cognizant_Toyota_JD_capture.md - Google Drive output used: No. The input JD was read from the user-provided Google Doc; no deliverable was written to Google Drive.