Looking for AI Engineer in Plano, TX (Onsite)

Saksoft; Plano (TX); On-site; USD 120,000 - 180,000; Full time

Job summary

Saksoft is seeking an experienced AI Engineer in Plano, TX, onsite for a contract role within the DSML Team. You will design, build, evaluate and operationalize the DSML AI Accelerator, focusing on governance, traceability and measurable delivery impact.

You will advance from discovery to a reusable, governed acceleration capability, working in two‑week sprints and collaborating with data scientists, ML Engineers and business stakeholders to deliver end‑to‑end DSML workflows and evaluation

Qualifications

• Bachelor's degree in CS/DS/ML or related field.
• 5 years of software, data science, ML or AI engineering experience including Python development.
• Hands‑on experience building LLM-enabled applications, RAG systems, agent workflows with APIs.
• Strong Python skills, CI/CD, API design, documentation and observability.

Responsibilities

• Governed Knowledge and RAG including onboarding documents, data definitions and expert knowledge into a governed knowledge base.
• Implement retrieval pipelines, metadata taxonomy, tagging, source citations and quality checks.
• Create evaluation datasets and retrieval quality metrics; diagnose relevance, grounding and source stewardship gaps.
• Define and instrument evaluation for agent outputs including correctness, completeness, traceability and cost-to-serve.
• Design multistep agent workflows that retrieve context, use tools safely and retain human review points.

Skills

Data science

Machine Learning

Geospatial

Education

Bachelor's degree in Computer Science, Data Science, Machine Learning or a related field

Job description

AI Engineer

Plano, TX (Onsite)

Contract

Job Details

• Client Data Science and Machine Learning DSML Team is seeking an experienced AI Engineer to design build evaluate and operationalize the technical foundation of the DSML AI Accelerator This 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 DSML 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 Youll 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 DSML context into structured usable outputs for data scientists and stakeholders
• Build the DSML UseCase 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 multistep agent workflows that retrieve approved context, use tools safely, produce structured outputs and retain human review points
• Develop the DSML 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

• Bachelors 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, CICD, 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 trade‑offs 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
• Engagement Structure and Deliverables
• Phase 1 Foundation – Prototype and Continuation Decision
• The role will establish the core technical foundation for the DSML AI Accelerator and deliver the capabilities needed to support a leadership decision on whether to continue and expand the effort
• Current state assessment of DSML context discovery pain points and pro

Skills

Mandatory Skills : Data science, ML & geospatial