AI Engineer - DS&ML

Actus Consulting Group; Plano (TX); Hybrid; USD 120,000 - 180,000; Full time

Job summary

Japanese IT company is seeking an experienced AI Engineer to design, build, evaluate, and operationalize the technical foundation of a DS/ML AI Accelerator. The role emphasizes hands-on generative AI engineering, RAG pipelines, and agent workflows with strong governance and traceability.

Hybrid work environment with four days in the office; temporary position from October 2026 to March 31, 2027. Candidates should have 5+ years in AI/ML engineering and a solid Python background.

Qualifications

• Bachelor's degree or equivalent practical experience in CS/DS/ML.
• 5+ years in software, data science, ML, or AI engineering with production Python and version-controlled delivery.
• Hands-on experience with LLM-enabled apps, RAG, agentic workflows, using APIs and structured tool integrations.
• Strong Python skills and modern software practices: testing, code review, CI/CD, API design, observability.
• Experience with data retrieval/indexing, embeddings, vector or hybrid search, evaluation design, and quality measurement.
• Ability to translate ambiguous business problems into testable technical requirements and communicate tradeoffs.

Responsibilities

• Governed knowledge and RAG: onboard documents and data, tagging, citations, quality checks.
• Design and build secure multi-agent workflows with explicit roles, tool contracts, and human review points.
• Define evaluation, backlogs, and sprint planning; ensure secure development and governance compliance.
• Collaborate with data owners and stakeholders to ensure traceability and repeatable outputs.

Skills

Python programming

LLM orchestration

RAG systems

API design

CI/CD

Data governance

Education

Bachelor's degree in Computer Science, Data Science, ML, or related field

Tools

Git

CI/CD tools

Vector databases

Embeddings / vector search

Job description

Position Summary:

Japanese IT company isseeking 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.

Position Type/Expected Hours of Work:

• Hybrid work environment (4 days a week in office)
• Temp position (Duration: October, 2026 – March 31st, 2027)

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

Minimum qualification:

Required Education & Experience:

• 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 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, 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.