Artificial Intelligence Engineer

Droisys is an innovation technology company focused on helping companies accelerate their digital initiatives from strategy and planning through execution. We leverage deep technical expertise, Agile methodologies, and data-driven intelligence to modernize systems of engagement and simplify human/tech interaction. Amazing things happen when we work in environments where everyone feels a true sense of belonging and when candidates have the requisite skills and opportunities to succeed. At Droisys, we invest in our talent and support career growth, and we are always on the lookout for amazing talent who can contribute to our growth by delivering top results for our clients. Join us to challenge yourself and accomplish work that matters. Artificial Intelligence Engineer Plano, TX [Remote Work] 6 Months [Potential Extensions] Rate: $60/hr W2 Who We’re Looking For Clients Data Science and Machine Learning (DSML) Team is seeking an experienced AI Engineer to design, build, evaluate, and operationalize the technical foundation of the DS/ML AI Accelerator. This contractor 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 DS/ML teams move 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. What You’ll Be Doing 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 Build the Context Agent, which retrieves and organizes approved DS/ML context into structured, usable outputs for data scientists and stakeholders. Build the DS/ML Use-Case Agent, which translates 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, use tools safely, produce structured outputs, and retain human review points. Develop the DS/ML Project Scaffolding Agent, which converts a use case into a standardized project starting point, generating the repository structure, starter code, documentation, templates, and workflow guidance needed to begin delivery efficiently. 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 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. What You Bring 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 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. Droisys is an equal opportunity employer. We do not discriminate based on race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law. Droisys believes in diversity, inclusion, and belonging, and we are committed to fostering a diverse work environment.