AI Engineer: Build Governed RAG & Agent Workflows (Onsite)

Saksoft; Plano (TX); Vor Ort; USD 120.000 - 180.000; Vollzeit

Zusammenfassung

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

Qualifikationen

• 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.

Aufgaben

• 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.

Kenntnisse

Data science

Machine Learning

Geospatial

Ausbildung

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

Jobbeschreibung

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