Senior AI/ML Engineer: LLM & Agent Stack
About this role
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Skills you'll use
What you'll do
- Architect and implement scalable agent orchestration patterns (graph-based executors, state management, multi-agent coordination) for production workloads.
- Own critical integrations: model adapters, LLM gateway hooks, vector DBs, tools & external APIs, and the platform’s LLMops flows.
- Build and improve tracing, benchmarking and observability for LLMs and agents, token/cost accounting, latency p95, throughput, and correctness checks.
- Drive design for safety/guardrails: moderation hooks, human-in-the-loop checkpoints, replayable audit trails and policy enforcement.
- Mentor junior engineers, run design reviews, and improve engineering practices (testing, CI/CD, chaos testing for agents).
- Work directly with strategic customers to prototype complex agentic solutions and translate them into product features.
- 4–10 years of software engineering with substantial experience building distributed systems, infra, or ML platforms.
- Deep practical experience integrating and deploying LLMs in production (RAG, retrieval, embeddings pipelines).
- Hands-on experience with agent orchestration frameworks (LangGraph / LangChain or custom agent runtimes) and stateful workflow design.
- Proven track record building observability, cost controls, and policy enforcement for production services.
Who can apply
- Open-source contributions, architecture blogs, or public talks on agentic LLMs or LLMops.
- Examples of productizing research or shipping complex infra features.
- Traits we are looking for: Ownership, ability to execute, hustle and think out of the box, data-driven decision making, be comfortable with more unknowns than knowns.
Perks & benefits
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