AI Engineer - DS&ML

Veritis Group Inc; Plano (TX); On-site; USD 150,000 - 190,000; Full time

Job summary

Veritis Group Inc. in Plano, TX seeks an experienced AI Engineer to design, build, and operate the AI accelerator's foundations, focusing on governance, RAG, and agent workflows.

You will implement retrieval pipelines, ensure outputs distinguish facts from inferences, and collaborate across data scientists, ML engineers, and SMEs to deliver measurable impact.

Qualifications

• Bachelor’s degree in CS, data science, ML, or equivalent practical experience.
• 5+ years in software, data science, ML, or AI engineering with production Python.
• Hands-on experience with LLM-enabled apps, RAG, and agentic workflows.
• Strong Python skills and modern software practices: testing, CI/CD, API design.
• Experience with data retrieval, embeddings, vector/hybrid search, and evaluation.
• Ability to translate business problems into testable technical requirements.
• Commitment to responsible AI, privacy, and data governance.

Responsibilities

• Onboard approved documents into a governed knowledge base.
• Build retrieval pipelines with metadata, tagging, and source citations.
• Create evaluation datasets and measure retrieval quality.
• Design secure multi-agent workflows with tool contracts and human review.
• Define agent roles, workflow state, and failure handling for reliability.
• Deliver reusable prompt and orchestration patterns for data teams.
• Work in two-week sprints, document decisions, and prioritize backlog.
• Ensure compliance with Responsible AI and enterprise governance.
• Partner with future operating owner for maintainable code and runbooks.

Skills

Python development

LLM integration

RAG systems

APIs & tool integrations

Embeddings & search

Responsible AI

Agile delivery

Communicate tradeoffs

Education

Bachelor’s degree in CS/DS/ML or equivalent

Tools

APIs

CI/CD

Version control

Observability

Job description

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. ]

Essential Functions

Description / Essential Functions

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.

Requirements

• 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 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.