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# AI Engineer - DS&ML

- **Company:** PROLIM Global Corporation
- **Location:** Plano, TX, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Clean Code Principles, Application Programming Interfaces (APIs), Artificial Intelligence, Audit Trail, Code Review, Continuous Integration, Data Governance, Data Retrieval, Python (Programming Language), Machine Learning, Metadata, Secure Coding, Software Engineering, Retrieval-Augmented Generation, Large Language Models, Generative AI, Agentic-AI, Build Management, Information Technology, Machine Learning Operations, Evaluation of Large Language Models, Api Design, Software Version Control, Human in the Loop
- **Published:** October 6, 2026
- **Apply:** https://www.dice.com/job-detail/f9ecf3b6-3abe-45db-b5f6-fd9e290661d2

## About the Role

* Bachelor's degree in Computer Science, Data Science, Machine Learning, or a related field, or equivalent practical experience.
 * 8+ 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.

 * 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.

## 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., * 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.