Staff AI Engineer, Function

• This is a hands-on role for a high-ownership engineer with deep systems expertise and a track record of delivering AI agents at scale
• 2.5+ years building agentic AI systems; 6+ years as a full-stack or ML engineer, building production backends or ML systems in Python, Go, or similar
• Fluency with agentic orchestration (e.g., LangGraph, PydanticAI, DSPy, LlamaIndex) and tool/function calling
• Experience integrating frontier LLMs and multimodal models via managed APIs or self-hosted serving
• Deep understanding of model serving and inference optimization (vLLM/Triton/TGI/SGLang, batching, KV cache reuse)
• Strong with API design and backend frameworks (FastAPI, Flask) and event-driven architectures
• Data systems expertise with PostgreSQL and Redis, including caching, token streaming, and throughput tuning
• Retrieval and memory: vector databases (pgvector, Pinecone, Weaviate, Milvus), hybrid search, and graph/knowledge storage
• Production evals: LLM-as-judge, human-in-the-loop, rubric design, and CI-integrated regression tests
• Observability and SRE: OpenTelemetry traces, metrics, structured logs, SLOs, dashboards, and on-call triage
• Cloud-native delivery: Kubernetes, Terraform, Docker, GPU scheduling/autoscaling on AWS or GCP
• CI/CD proficiency with GitHub Actions and test automation for prompts, tools, and agents
• Clear, concise communication and high ownership in fast-paced environments

Desirable

• You will design, ship, and scale production-grade, stateful multi-agent systems end to end—spanning orchestration graphs, model serving, real-time inference, and observability
• You’ll partner with product, infra, and research to integrate LLMs and multimodal models (voice, vision, structured data) into consumer and internal workflows with strong safety, reliability, and cost controls
• Ship agentic features that move core product KPIs with measurable quality and latency targets
• Establish evaluation gates and on-call reliability for AI systems that handle real users and revenue
• Reduce cost-to-serve via model routing, KV cache reuse, and retrieval quality improvements
• Architect and build stateful, graph-based agent workflows with tool use, planning, and memory
• Integrate LLMs and multimodal models via structured I/O (JSON Schema, Pydantic validators) and function/tool calling
• Build high-reliability APIs and streaming services for real-time inference, speech, and vision
• Own production readiness: tracing, logging, metrics, rate limiting, circuit breakers, and SLOs
• Stand up eval pipelines: offline golden sets, LLM-as-judge with human rubrics, online A/B, and regression tests in CI
• Implement retrieval and memory: hybrid search, vector and graph retrieval, semantic caches, and long-horizon context
• Optimize cost/latency: model routing, prompt and tool selection, quantization, and KV cache/prefill strategies
• Lead cloud-native deployments on Kubernetes with GPU autoscaling, canary/shadow releases, and feature flags
• Partner cross-functionally to translate research into robust production systems and iterate quickly behind evaluation gates
• Mentor engineers through code reviews, design docs, and architecture decisions
• Example Tech You'll Touch:
• Orchestration: LangGraph, PydanticAI, DSPy, LlamaIndex
• Serving: vLLM, Triton, TGI, SGLang; OpenAI/Anthropic-compatible APIs
• Backend: Python, Go, FastAPI, gRPC, Kafka/PubSub
• Data: PostgreSQL, Redis, pgvector, Pinecone/Milvus/Weaviate
• Observability: OpenTelemetry, Prometheus, Grafana, Sentry
• Infra: Kubernetes, Terraform, Docker, GPU operators, Karpenter/Cluster Autoscaler
• Evals & QA: RAGAS/DeepEval-style frameworks, golden sets, canary/shadow testing
• How We Build:
• Evaluation-driven development: every change to prompts, tools, routing, or retrieval passes automated eval gates
• Structured outputs by default: JSON Schema/Pydantic validation, strict tool contracts, and idempotent handlers
• Safety-first tooling: guardrails, content and data policies, tool sandboxing with timeouts and scopes
• Pragmatic iteration: short cycles, feature flags, shadow traffic, and fast rollback
• Success in 90 Days:
• Launch a production agentic workflow with clear SLOs, tracing, and dashboards
• Stand up an eval harness with golden sets and CI gates for the top use case
• Improve latency and cost with routing and KV cache strategies while maintaining quality