Lead design and build scalable autonomous and semi‑autonomous multi‑agent AI systems. Implement RAG pipelines, tool‑calling agents, memory, observability, and AgentOps. Ensure production readiness, safety, governance, and secure data handling. Provide technical leadership, mentor engineers, and shape platform standards across teams.
We are seeking a Lead Agentic AI Engineer with a strong software engineering foundation to design, build, and scale autonomous and semi‑autonomous AI systems. This role goes beyond prompt engineering or isolated models—you will architect multi‑agent systems that plan, reason, call tools, interact with enterprise systems, and operate safely at scale.
You will work on agent orchestration, RAG pipelines, tool‑calling, memory, observability, and AgentOps, while ensuring production readiness, compliance, and reliability.
Agentic AI System Design
- Design and implement multi‑agent architectures including:
- Orchestrator / supervisor agents
- Task‑specialized agents (research, extraction, validation, decisioning)
- Reflection, critique, and self‑correction loops
- Build goal‑driven agent workflows with constrained autonomy and human‑in‑the‑loop patterns.
- Define agent boundaries, decision policies, and escalation logic.
LLM, RAG & Tooling
- Build enterprise‑grade RAG systems:
- Ingestion, chunking, metadata enrichment
- Vector indexing and retrieval strategies
- Grounded generation and citation control
- Implement tool‑calling agents that interact with:
- APIs, databases, search systems
- Internal platforms and workflows
- Optimize latency, cost, and accuracy across LLM interactions.
Production Engineering & AgentOps
- Build agentic systems as scalable backend services (FastAPI / REST / async services).
- Apply strong software engineering discipline:
- Modular code, clean abstractions, testability
- CI/CD, versioning of prompts, tools, and agents
- Implement AgentOps / LLMOps, including:
- Evaluation harnesses (prompt, retrieval, agent behavior)
- Observability (traces, metrics, decision paths)
- Rollback and controlled rollout strategies
- Ensure robustness against hallucinations, loops, tool failures, and unsafe actions.
Governance, Safety & Reliability
- Implement guardrails, policies, and monitoring for agent behavior.
- Design systems with traceability, auditability, and explainability.
- Ensure secure handling of sensitive data (PII/PHI where applicable).
- Enforce responsible‑AI principles in autonomous systems.
Technical Leadership
- Lead design reviews and mentor junior engineers.
- Influence platform standards for agentic AI across teams.
- Partner with product managers, architects, and domain SMEs to translate workflows into agent behavior.
Strong Software Engineering Background (Must Have)
- 8–10 years of experience with backend/software engineering
- Expert in Python (primary) and API‑based services
- Experience with distributed systems, microservices, async processing
- Strong understanding of system design, scalability, and performance
Agentic AI & GenAI Expertise
- Proven experience building agentic AI systems (not just chatbots)
- Hands‑on with agent frameworks (e.g., LangChain / LangGraph / equivalent)
- Strong understanding of:
- Tool‑calling, planning, reflection
- Memory, state, and long‑running agents
- Experience working with LLMs (cloud and/or open‑source)
RAG & Data Engineering
- Production experience with vector databases
- Knowledge of embeddings, retrieval strategies, reranking
- Comfortable with structured + unstructured enterprise data
Platform & Ops
- Docker, CI/CD, cloud deployments (AWS / Azure / GCP)
- Experience with monitoring, logging, and production debugging
- Familiarity with MLflow or similar experiment tracking tools
Nice to Have
- Experience in healthcare, insurance, or regulated domains
- Exposure to AI governance, compliance, or risk frameworks
- Built internal AI platforms or reusable accelerators
- Prior experience tech‑leading enterprise AI initiatives
What you need to know about the Los Angeles Tech Scene
Los Angeles is a global leader in entertainment, so it’s no surprise that many of the biggest players in streaming, digital media and game development call the city home. But the city boasts plenty of non-entertainment innovation as well, with tech companies spanning verticals like AI, fintech, e-commerce and biotech. With major universities like Caltech, UCLA, USC and the nearby UC Irvine, the city has a steady supply of top-flight tech and engineering talent — not counting the graduates flocking to Los Angeles from across the world to enjoy its beaches, culture and year-round temperate climate.
Key Facts About Los Angeles Tech
- Number of Tech Workers: 375,800; 5.5% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Snap, Netflix, SpaceX, Disney, Google
- Key Industries: Artificial intelligence, adtech, media, software, game development
- Funding Landscape: $11.6 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Strong Ventures, Fifth Wall, Upfront Ventures, Mucker Capital, Kittyhawk Ventures
- Research Centers and Universities: California Institute of Technology, UCLA, University of Southern California, UC Irvine, Pepperdine, California Institute for Immunology and Immunotherapy, Center for Quantum Science and Engineering
