AI Engineer

Sensiple Inc is a New Jersey corporation with over two decades of expertise in technology-driven solutions specializing in Customer Experience, Contact Center Solutions, Digital Transformation, Cloud Computing & Independent Testing.

With an expert team that has enriched experience in executing & developing sustainable IT strategies in Healthcare, Technology, Retail, Logistics, Education, Telecommunications, Government and Media, we help our diverse customers to envision the future.

By developing highly scalable and consistent solutions, our primary goal is to deliver excellence at all levels and delight our customers and drive them to a better future.

Position: AI Engineer

Work Location: Plano, TX (Onsite)

Duration: Long Term Contract (W2)

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.

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 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.

Required 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 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.