GenAI Platform Engineer

Build AI that matters

At Accenture Federal Services (AFS), we turn GenAI from demos into operational capabilities that millions rely on. We ship production GenAI apps for confidential federal programs across defense, national security, public safety, civilian, and military health where reliability, privacy, and safety aren't optional.

Role Overview

• Operationalize LLM/RAG: Stand up and harden AI services (cloud or on-prem) that ground responses in mission data with low hallucination rates.
• Guardrails & governance: Implement prompt/version management, policy-based filtering, role-based access, audit trails, and safety checks aligned to federal guidelines.
• Observability & SRE for AI: Define SLIs/SLOs (quality, latency, safety, cost), instrument metrics, run on-call, lead postmortems, and reduce MTTR.
• Performance & cost engineering: Optimize p95 latency with caching/batching/autoscaling; meter usage and apply FinOps to keep token/spend within targets.
• Reusable platform components: Ship SDKs, CI/CD templates, and Terraform/IaC modules that accelerate multiple mission teams not one-off projects.
• Applied theory (where it matters): Use IR metrics (e.g., NDCG) to improve retrieval quality; apply queueing/control principles to hit latency SLOs; run offline/online evals for helpfulness, safety, and factuality.

What you won't do: Train or invent new foundation models; deliver academic research. Your focus is making best-in-class models work flawlessly in production.

You'll Thrive Here If You

• Own production systems end-to-end: Integration → deployment → observability → incident response.
• Are strong in Python
• Have experience with retrieval/vector search (e.g., pgvector/Milvus/OpenSearch) and grounding AI in enterprise data.
• Think in SLOs and dashboards; you improve reliability, latency, safety, and cost with data.
• Communicate clearly with engineers, PMs, and security stakeholders especially in compliance-heavy environments.
• Must be a US Citizen

Nice to Have

• Experience integrating leading cloud AI services or on-prem inference stacks Background in evaluation/safety testing or A/B experimentation for AI experiences.
• FinOps for AI: metering, budgets, and optimization strategies for usage and spend.