Ai Platform Engineer (Agentic)
Skills
About This Role
Key Responsibilities
- Design and implement the multi-agent orchestration framework using frameworks such as LangGraph, CrewAI, AutoGen, or custom Python.
- Build the model-serving and routing layer supporting multiple LLM backends (self-hosted open-weights models and approved API providers), with caching, fallback and cost tracking.
- Implement tool integrations: SIEM APIs, Tenable, ExtraHop, GitLab, Fortify, sandbox detonation, shell/code execution sandboxes, MITRE ATT&CK/D3FEND knowledge bases.
- Build vector stores, RAG pipelines and long-term memory for agents; maintain curated security knowledge corpora.
- Engineer the evaluation harness: golden datasets, regression tests, red-team prompts, quality and safety metrics per agent.
- Implement guardrails and AI security controls per PLOT4AI, OWASP LLM Top 10, MITRE ATLAS and NIST AI RMF: prompt-injection defence, output filtering, tool-use authorization, data exfiltration controls, model supply-chain verification.
- Package and deploy the platform on private cloud with full observability (traces, token usage, cost, latency, safety events).
- Partner with the Principal AI Security Architect on threat modelling of the platform itself.
Goals
- Deliver a production-grade agentic AI platform that the three agent lines (SOC, Pentest, Code Review) can build on without reimplementing common primitives.
- Ensure every deployed agent runs under enforced guardrails, authorization boundaries, and full audit logging.
- Achieve reproducible agent quality through a rigorous evaluation and regression framework no silent regressions between model or prompt updates.
- Keep infrastructure and model costs transparent and under budget through routing, caching and quota controls.
Specific Objectives (SMART)
- Within 30 days: publish architecture, select orchestration framework, stand up dev environment, and deliver a hello agent end-to-end trace.
- Within 60 days: deliver v0.1 of the platform with tool-use, RAG, guardrails and evaluation harness; onboard the AI SOC agent as the first tenant.
- Within 90 days: deliver v0.2 supporting the AI Pentest and AI Secure Code Review agents; publish threat model and AI RMF mapping.
- Within 6 months: production-grade v1.0 with SLOs, on-call runbooks, and red-team validation.
Timeline & Engagement Model
- 12-month contract.
- Design + MVP: Months 1-3.
- Multi-agent support: Months 3-6.
- Production hardening: Months 6-12.
Required Skills & Experience
- 5+ years backend engineering in Python (FastAPI, async, typing); strong systems design.
- Hands-on experience building LLM agent systems with LangGraph / LangChain / CrewAI / AutoGen or equivalent.
- Production experience with vector DBs (pgvector, Qdrant, Weaviate), embedding pipelines, and RAG.
- Strong MLOps / LLMOps: model serving (vLLM, TGI, Ollama), evaluation frameworks, tracing (OpenTelemetry, LangSmith, Langfuse).
- Cloud-native delivery: Docker, Kubernetes, Helm, CI/CD via GitLab.
- Working knowledge of OWASP LLM Top 10, NIST AI RMF, MITRE ATLAS, PLOT4AI.
- Security mindset: secrets handling via Vault, least-privilege tool access, auditability.
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