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.

Qualifications/Skills

• 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).

To Be a Strong Fit

• Ruthless Prioritization:
• Member-First, Always:
• One Team, Moving Fast:
• Radical Ownership, Relentless Execution:
• Mission Over Ego:
• Sustained Integrity in Every Detail: