Senior Applied AI Engineer

Key Responsibilities

• 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
• Partner cross-functionally to translate research into robust production systems and iterate quickly behind evaluation gates
• Mentor engineers through design docs and architecture decisions

Requirements

• 1+ 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
• Strong with API design and backend frameworks (FastAPI, Flask) and event-driven architectures
• Data systems expertise with PostgreSQL, including 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

Nice to Haves

• Real-time multimodal systems: streaming ASR, low-latency TTS, WebRTC, and vision pipelines
• RAG expertise beyond basics: Graph RAG, multi-hop retrieval, sub-agents, query planning, and freshness policies
• Safety and governance: policy-as-code, red-teaming, PII handling, audit logs, and role-based tool authorization
• Regulated data experience (HIPAA, SOC 2, GDPR) and data residency controls
• Personalization at inference time, long-term memory agents, session state, and episodic memory stores
• Experience with consumer-scale AI apps, high-traffic systems, or on-device/edge acceleration (WebGPU)