We’re looking for an experienced AI Engineer who is passionate about building intelligent, tool-using agents leveraging cutting-edge LLM orchestration frameworks. You will be a core contributor to the design, development, and deployment of agentic systems that combine reasoning, memory, retrieval, and orchestration to power the next generation of enterprise AI applications.
Core Technical Responsibilities Agentic Design- Proven ability to architect multi-step agents with planning, tool usage, and self-reflection.
- Experience with orchestration frameworks like LangGraph, CrewAI, or custom graph/task-based planners.
- Deep understanding of Chain, Runnable, and LCEL abstractions.
- Built custom output parsers, retrievers, and memory components.
- Experience with tracing & evaluation tools such as LangSmith, Langfuse, or equivalents.
- Implemented short-term memory (e.g., message history, token windowing).
- Designed long-term memory using vector stores (FAISS, Pinecone, Milvus, OpenSearch) or knowledge graphs.
- Strong grasp of performance/cost trade-offs across memory backends.
- Designed hybrid retrieval pipelines (BM25 + dense vector search).
- Familiar with chunking/embedding strategies, adaptive retrieval, and reranking.
- Skilled in structured prompting (JSON/YAML schemas, function-calling).
- Experienced in dynamic prompt injection for real-time tool use.
- Knowledge of automated evaluation techniques (rubric-based, self-critique).
- Familiar with red-teaming, toxicity, bias detection, and PII leakage prevention.
- Proficient in Python 3.11+ (typing, Pydantic, asyncio) and TypeScript.
- Test-driven development with pytest, coverage tools, and LangChain-specific testing patterns.
- Experienced in working with agentic coding tools like Claude Code, Kiro, Copilot,...
- Experience in vector-store migrations, prompt/model versioning, and model registries.
- Monitoring with OpenTelemetry, CloudWatch, Honeycomb, or similar observability stacks.
