Staff AI Platform Engineer - Inference & Agentic Systems

What You'll Do

• Inference & Model Serving
• Build and operate multi-model serving across modalities (text, voice, code, vision) on shared infrastructure
• Own the model lifecycle: download, deploy, serve, monitor, update, swap
• Drive inference optimization: latency, throughput, cost - including quantization, batching, caching, and routing strategies
• Ensure inference is fast and reliable for the agents and systems that depend on it


• Agentic Systems
• Architect and build the Agentic AI Platform - runtime infrastructure, orchestration systems, and developer tooling for autonomous agents
• Design multi-agent coordination systems enabling agents to collaborate and solve complex workflows
• Build robust tool-use infrastructure that allows agents to interact with APIs, databases, and services safely
• Implement workflow automation: agents that execute multi-step business and engineering tasks with appropriate guardrails
• Build safety and guardrail systems including permissioning, sandboxing, and human-in-the-loop workflows
• Develop evaluation and observability frameworks to measure agent behaviour, detect regressions, and debug failures
• Develop SDKs and APIs that allow internal teams to build and deploy agents quickly and safely


• Platform & Technical Leadership
• Define technical direction and architecture for agentic systems across the organization
• Build patterns and standards for agent design, tool calling, and evaluation
• Partner closely with ML, product, and security teams to deliver production-grade agent systems
• Mentor engineers and contribute to best practices for agent system design

What You'll Bring

• 8+ years of software engineering experience, with 3+ years in AI systems or LLM applications
• Strong understanding of LLM-based agent architectures: tool use, multi-step workflows, multi-agent coordination, and their failure modes
• Experience building highly reliable distributed systems
• Experience evaluating LLM systems in production: building evals, detecting regressions, and debugging non-deterministic failures
• Proficiency in TypeScript or Python, and willingness to work in both: the agent platform is TypeScript on Bun with Temporal workflows on Kubernetes and EC2, the inference platform is Python.
• Experience working with modern LLM APIs or open-source models 
• Experience with or strong interest in model serving (vLLM, TensorRT-LLM, Triton)
•  Understanding of distributed systems: task queues, event-driven architectures, state management, and durable long-running workflows 
• Experience with cloud platforms (AWS, GCP) and containerized deployments
• Strong understanding of security risks in agentic systems (prompt injection, privilege escalation, data leakage) 
• Demonstrated experience leading complex technical initiatives
• Strong written and verbal communication skills 

Nice to Have

• Experience building agentic systems in regulated industries (fintech, healthcare, enterprise)
• Familiarity with Model Context Protocol (MCP) or agent communication standards
• Experience with model fine-tuning, quantization, or LoRA 
• Experience building CI/CD automation and developer tooling 
• Experience adapting workflow orchestration systems (Temporal, Airflow, Prefect) for AI workloads 
• Experience with voice models, multimodal models, or edge inference 
• Experience designing human-in-the-loop or oversight systems