AI Engineer - DS&ML

Job Title: AI Engineer - DS&ML
Location: ENGINEERING DIVISION Plano, Texas, United States
Duration:

Long term
Experience:10 Above

Position Summary:

Job Title: AI Engineer - DS&ML
Location: ENGINEERING DIVISION Plano, Texas, United States
Duration:

Long term
Experience:10 Above

Position Summary:

We are seeking an experienced AI Engineer to design, build, evaluate, and operationalize the technical foundation of the DS/ML AI Accelerator. The successful candidate will be responsible for 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.].

• 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
  • Design and build secure multi‑agent workflows that retrieve approved context, coordinate LLM and deterministic steps, use tools safely, and produce structured, traceable outputs.
  • Define explicit agent roles, tool contracts, workflow state, validation, error handling, stop conditions, and human review points for reliable multi‑agent operation.
  • Implement reusable 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.

Minimum qualification:

Required Education & Experience

• 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.

• Hybrid work environment (4 days a week in office)
• Contract position (Duration: October, 2026 March 31st, 2027)