Our client, a Series B company automating decision-making for field workers at scale, is looking for a Senior AI Platform Engineer to own the infrastructure layer enabling autonomous agents to operate reliably at production scale. You’ll design and build the systems that allow their applied AI team to ship production LLM applications without breaking things. The focus is infrastructure engineering for AI systems — not traditional MLOps or feature engineering.
Multi-modal data pipelines — Ingesting video, audio, and structured data at scale; Agentic orchestration on serverless AWS — Building Lambda + Step Functions infrastructure for autonomous workflows; Observability and guardrails — Implementing monitoring that catches when autonomous agents fail; tracking decision quality, tool-use patterns, and failure modes 4+ years building production systems (not just experiments or prototypes)~ Strong Python skills; Deep AWS infrastructure knowledge — particularly serverless (Lambda, Step Functions, SQS, EventBridge)~ Hands-on experience with agentic AI systems, LLM orchestration frameworks (LangGraph, CrewAI, etc.), Platform engineer mindset — you think about schema design, API stability, backward/forward compatibility, and developer experience
Experience with multi-modal systems (video, audio, or image processing) AWS Bedrock, SageMaker, or similar managed AI services RAG pipelines, vector databases, or retrieval-augmented systems You’re not building features. You’re building the infrastructure that enables autonomous decision-making at scale . This requires thinking like a platform engineer: How does data flow through the system without corruption? You’ve shipped production systems. You’re comfortable with serverless, Python, and the AWS stack. You’re not intimidated by agentic AI systems — you understand they’re just orchestration patterns, and infrastructure principles remain the same….