AI Agent Software Engineer

We’re looking for a Software Engineer specializing in AI agent development to design, build, and deploy autonomous and semi-autonomous AI systems. You’ll work at the intersection of large language models, tool use, and software engineering to create agents that can reason, plan, and execute complex tasks., * Design and develop AI agents using frameworks like LangChain, LangGraph, CrewAI, AutoGen, or the Anthropic/OpenAI SDKs

• Build multi-agent systems with orchestration, memory, and tool-use capabilities
• Integrate LLMs (Claude, GPT, Llama, etc.) with external APIs, databases, vector stores, and MCP servers
• Implement RAG (Retrieval-Augmented Generation) pipelines using vector databases like Pinecone, Weaviate, Chroma, or pgvector
• Develop and optimize prompts, system instructions, and agent workflows
• Build evaluation harnesses to test agent reliability, accuracy, and safety
• Implement guardrails, observability, and monitoring for agents in production
• Collaborate with product, design, and ML teams to translate requirements into agent capabilities
• Handle latency, cost optimization, token usage, and model selection trade-offs
• Stay current with rapidly evolving AI tooling and best practices

Do you have experience in Version control systems?, * 3+ years of professional software engineering experience

• Strong proficiency in Python (and ideally TypeScript/JavaScript)
• Hands-on experience building applications with LLM APIs (Anthropic, OpenAI, Google, or open-source models)
• Experience with at least one agent framework (LangGraph, LangChain, CrewAI, AutoGen, Semantic Kernel, etc.)
• Understanding of prompt engineering, function/tool calling, and structured outputs
• Experience with RAG systems and vector databases
• Familiarity with async programming, API design, and microservices
• Solid understanding of software engineering fundamentals: testing, version control, CI/CD, * Experience deploying agents to production at scale
• Knowledge of Model Context Protocol (MCP) and tool integration patterns
• Background in ML/NLP or fine-tuning techniques (LoRA, RLHF basics)
• Experience with evaluation frameworks (LangSmith, Braintrust, custom evals)
• Familiarity with cloud platforms (AWS, GCP, Azure) and containerization (Docker, Kubernetes)
• Contributions to open-source AI projects
• Understanding of AI safety, alignment, and responsible AI practices

Tech Stack You’ll Work With

• Python
• TypeScript
• FastAPI
• LangGraph
• Anthropic/OpenAI SDKs
• vector databases
• PostgreSQL
• Redis
• Docker
• AWS/GCP
• observability tools (Langfuse, Helicone, etc.)