We're looking for a hands-on
AI Engineer
who combines strong backend engineering fundamentals with hands-on experience building production Generative AI systems. You'll design and ship RAG pipelines, integrate LLMs into real products, and build the backend services that support them, writing code daily, not just architecting on paper.
This is a purely technical IC role, not a managerial one. You’ll lead by example, mentor through code reviews, and own end-to-end technical delivery.
Key Responsibilities
Design and build RAG systems, embeddings, vector search, chunking, and evaluation pipelines.
Build and maintain multi-agent orchestration workflows (LangGraph, AutoGen, CrewAI, or similar).
Develop backend services and APIs (Python — Flask/FastAPI) that expose AI workflows to production systems.
Deploy and scale AI workloads in cloud-native environments, using serverless or containerized patterns.
Implement LLMOps practices: prompt versioning, cost tracking, monitoring, and evaluation.
Write clean, tested code, and use AI-assisted tools (Copilot, Cursor, Claude Code) to move faster without cutting corners.
Work with data and platform engineers to ship GenAI features quickly, from prototype to production.
Skills, Knowledge and Expertise
Must-Have Skills
5+ years of backend experience, with strong Python coding skills.
Proven experience shipping RAG systems (vector DBs, embeddings, chunking).
Familiarity with orchestration frameworks (LangGraph, LangChain, AutoGen, or similar).
Experience with APIs, microservices, and cloud-native development (AWS preferred).
Familiarity with distributed systems concepts (async, message queues, caching).
Nice-to-Have
Experience with unstructured data (PDFs, tables, images).
Soft Skills
Builder mindset: thrives on writing, debugging, and improving production code.
Collaborative, humble, and open to feedback.
Strong communicator who explains design decisions clearly.
Influences through contribution, not hierarchy.