DSML Engineer – Generative AI

DSML Engineer – Generative AIAbout 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.