AI Engineer (Agentic LLM Systems)

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.

• 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.

• Exposure to OpenAI Assistants API, Semantic Kernel, or open-source agent stacks (AutoGen, SuperAGI, etc.)
• Knowledge of enterprise AI deployment patterns (multi-tenant RAG, ACL for memory/knowledge base).
• Familiarity with GPU cost optimization and caching strategies.
• Experience with other programming languages such as Java and Go (Golang).
• AWS DevOps experience, including CI/CD pipelines, monitoring, and infrastructure automation.
• Domain knowledge in financial services or banking is a strong plus.

• Opportunity to work at the frontier of AI agent systems with a high-impact product.
• Collaborative environment with strong engineering autonomy.
• Access to cutting-edge AI infrastructure and tooling.
• Competitive compensation and flexible working model.