DSML Engineer - Onsite

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

Job summary

Saksoft is seeking a DSML Engineer in Plano, TX to accelerate governance-enabled, reusable DSML capabilities. The role focuses on building agentic workflows, retrieval pipelines, and scalable project scaffolding for data science use cases.

Ideal candidates have 5+ years of software/ML experience, strong Python skills, and a track record with LLM-enabled applications, RAG systems, and responsible AI practices. Long-term, onsite position in Plano, TX.

Qualifications

• Bachelor's degree in CS/DS/ML or equivalent practical experience.
• 5+ years of software, data science, ML, or AI engineering experience with production-quality Python development.
• Hands-on experience building LLM-enabled applications, RAG systems, or agentic workflows using APIs and structured tool integrations.
• Strong Python skills and modern software engineering practices (testing, CI/CD, API design, documentation, observability).
• Experience with data retrieval, embeddings, vector/hybrid search, evaluation design, and quality measurement.

Responsibilities

• Partner with data and business owners to onboard approved knowledge into a governed knowledge base.
• Implement retrieval pipelines, metadata tagging, source citations, and quality checks for trustworthy results.
• Create evaluation datasets and metrics; diagnose gaps in relevance and grounding.
• Build and refine agents to retrieve and organize DSML context for data scientists and stakeholders.
• Design multi-step agent workflows, prompt patterns, and tool orchestration for repeatability and governance.

Skills

GenAI - LLMOps

Statistics-Data Science

Education

BSc in Computer Science/Data Science/ML or equivalent

Job description

Job Description

DSML Engineer – Generative AI: About the Role

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.

Position: DSML Engineer

Location: Plano, TX

Duration: Long-term

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, and diagnose gaps in relevance, grounding, completeness, and source stewardship.
• Ensure generated outputs clearly distinguish retrieved facts, inferences, assumptions, and items that require subject‑matter‑expert (SME) confirmation.

Agentic Product Engineering

• Context Agent: Build an agent that retrieves and organizes approved DSML context into structured, usable outputs for data scientists and stakeholders.
• DSML Use‑Case Agent: Build an agent that 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.
• DSML Project Scaffolding Agent: Develop an agent that 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.
• Design multi‑step agent workflows that retrieve approved context, use tools safely, produce structured outputs, and retain human review points.
• 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 of agent outputs, covering 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, including testing, code review, CI/CD, API design, documentation, and observability.
• Experience with data retrieval and indexing concepts, embeddings, vector or hybrid search, evaluation design, and quality measurement.
• Ability to turn ambiguous business problems into testable technical requirements and to 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, such as 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.

Skills

Mandatory Skills: GenAI - LLMOps, Statistics-Data Science