AI Engineer - DS&ML

Orangepeople•Plano, TX

11d

About The Position

We are seeking an experienced AI Engineer to design, build, evaluate, and operationalize the technical foundation of the DS/ML AI Accelerator. The role focuses on hands-on generative AI engineering, retrieval-augmented generation (RAG), agent workflow design, and software engineering, with an emphasis on governance, traceability, and measurable delivery impact. The AI Engineer will build governed knowledge and retrieval systems, secure multi-agent workflows, and reusable engineering patterns. The role also defines technical and user-centered evaluation, delivers working increments through two-week agile sprints, and supports responsible AI, security, privacy, and data-governance requirements. The engineer will partner with the future operating owner on maintainability, runbooks, monitoring, knowledge-source refresh practices, and enhancement planning. OP is a technology consulting and solutions company providing advisory and managed services, platforms, and staffing across fields including AI, cybersecurity, and enterprise architecture. The company describes its approach as a lean consulting structure focused on delivering results at pace.

Requirements

Bachelor's degree in Computer Science, Data Science, Machine Learning, or a related field, or equivalent practical experience.
At least 5 years of software, data science, machine learning, or AI engineering experience.
Production-quality Python development experience 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 testing, code review, CI/CD, API design, documentation, and observability.
Experience with data retrieval and 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.

Nice To Haves

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.

Responsibilities

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 to make context discoverable and trustworthy.
Create evaluation datasets and retrieval-quality metrics, and diagnose relevance, grounding, completeness, and source-stewardship gaps.
Ensure generated outputs distinguish retrieved facts, inferences, assumptions, and items requiring subject-matter-expert confirmation.
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 agent roles, tool contracts, workflow state, validation, error handling, stop conditions, and human review points.
Implement reusable prompt, workflow, and tool-orchestration patterns for Data Scientists, ML Engineers, Business Analysts, Product Owners, and SMEs.
Define and instrument technical and user-centered evaluation of 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.
Participate in OP monthly team meetings and team-building efforts.
Contribute to OP technical discussions and peer reviews.
Contribute content and collaborate via the OP-Wiki/Knowledge Base.
Provide status reports to OP Account Management as requested.