Ai Platform Engineer (Agentic)

Job Description Roles & Responsibilities 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. Desired Candidate Profile 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. Company Industry IT - Software Services Department / Functional Area IT Software Keywords Ai Platform Engineer (Agentic) Get real-time job updates only on our App