DSML Engineer - Onsite

Position: DSML Engineer

Location: Plano, TX

Duration: Long-term

JOB DESCRIPTION:

DSML Engineer – Generative AI: About the Role

The successful candidate will help DSML 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, and diagnose gaps in relevance, grounding, completeness, and source stewardship.
• Ensure generated outputs clearly distinguish retrieved facts, inferences, assumptions, and items that require subject-matter-expert (SME) confirmation.

Agentic Product Engineering

• Context Agent: Build an agent that retrieves and organizes approved DSML context into structured, usable outputs for data scientists and stakeholders.
• DSML Use-Case Agent: Build an agent that 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.
• DSML Project Scaffolding Agent: Develop an agent that 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.
• Design multi-step agent workflows that retrieve approved context, use tools safely, produce structured outputs, and retain human review points.
• 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 of agent outputs, covering 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, including 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 to communicate trade-offs to technical and non-technical 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, such as 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.

Skills

Mandatory Skills: GenAI - LLMOps, Statistics-Data Science