GEN AI Engineer

Galent; Plano (TX); On-site; USD 140,000 - 210,000; Full time

Job summary

Galent is seeking an AI Engineer to design and operationalize the DSML AI Accelerator's technical foundation, focusing on hands-on generative AI engineering, RAG, and governance.

You will build governed knowledge pipelines, agent workflows, and scaffolding to translate business objectives into structured data science outputs, with emphasis on traceability and measurable delivery impact.

Qualifications

• 5+ years in software, data science, ML, or AI engineering.
• Production Python solutions in version-controlled environments.
• Experience with LLMs, retrieval, and governance.
• Strong communication with technical and non-technical stakeholders.
• Commitment to responsible AI, privacy, security, governance.

Responsibilities

• Design and implement governed knowledge and RAG pipelines.
• Develop agent workflows and DSML project scaffolding.
• Define evaluation metrics for agent outputs and delivery impact.
• Collaborate with data owners and future operating owners.
• Ensure security, privacy, and enterprise governance compliance.

Skills

LLM apps

RAG systems

Agent workflows

APIs

Testing

Documentation

Observability

Data retrieval

Vector search

Security & governance

Education

Bachelor's degree in CS/DS

Tools

APIs

Tool integrations

Job description

The Data Science and Machine Learning (DSML) Team is seeking an experienced AI Engineer to design, build, evaluate, and operationalize the technical foundation of the DSML AI Accelerator. This 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 DSML teams transition 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.

Key Responsibilities

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 to make context discoverable and trustworthy.
• Create evaluation datasets and retrieval-quality metrics to diagnose relevance, grounding, completeness, and source stewardship gaps.
• Ensure generated outputs clearly distinguish retrieved facts, inferences, assumptions, and items requiring subject matter expert confirmation.

Agentic Product Engineering

• Build the Context Agent to retrieve and organize approved DSML context into structured outputs for data scientists and stakeholders.
• Build the DSML Use Case Agent to translate 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, safely use tools, produce structured outputs, and incorporate human review points.
• Develop the DSML Project Scaffolding Agent to convert a use case into a standardized project starting point by generating repository structures, starter code, documentation templates, and workflow guidance.
• 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 metrics 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 future operating owners to define maintainable code, runbooks, monitoring, knowledge source refresh practices, and enhancement backlogs.

Phase 1 Foundation, Prototype and Continuation Decision

• Establish the core technical foundation for the DSML AI Accelerator.
• Deliver capabilities necessary to support a leadership decision regarding continuation and expansion of the initiative.
• Conduct current-state assessment of DSML context discovery pain points and opportunities.

Required Skills

• Hands-on experience building LLM-enabled applications, RAG systems, agentic workflows, or AI assistants using APIs and structured tool integrations.
• Experience with:
• Testing and code reviews
• Documentation
• Observability
• Experience with data retrieval and indexing concepts.
• Knowledge of embeddings, vector search, and hybrid search.
• Experience designing evaluation frameworks and quality measurement methodologies.
• Ability to translate ambiguous business problems into testable technical requirements.
• Strong communication skills with both technical and non-technical stakeholders.
• Commitment to responsible AI, privacy, security, and data governance practices.

Preferred Skills

• Experience with modern LLM orchestration, agent workflow, or developer assistance platforms.
• LLM evaluation frameworks
• Guardrails
• Telemetry
• Experience supporting:
• ML lifecycle workflows
• Familiarity with:
• Data governance
• Responsible AI practices
• Access control
• Auditability

Qualifications

• 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.
• Proven experience delivering production-quality Python solutions in collaborative, version-controlled environments.

We are an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, citizenship status, age, disability, genetic information, protected veteran status, or any other characteristic protected by applicable law.

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