Senior AI Developer

Responsibilities

• Design and develop AI agents and autonomous multi-agent systems using modern agentic frameworks including LangGraph and LangChain
• Build and orchestrate multi-agent pipelines—including supervisor agents, collaborative agent networks, and hierarchical agent architectures
• Implement guardrails, reasoning workflows, and ReAct-based patterns within LangChain/LangGraph
• Develop memory management (short-term, long-term, episodic) and tool-use capabilities for AI agent systems
• Leverage LangGraph's stateful graph execution model to build resilient, interruptible, and human-in-the-loop agentic workflows
• Integrate LLM-powered agents with external APIs, databases, and enterprise data platforms via LangChain's retrieval, routing, and chain composition primitives
• Partner closely with prompt engineers, data scientists, and platform teams to optimize AI application performance
• Build and maintain scalable Python-based services, APIs, and microservices that serve as agent execution environments and tool backends
• Develop and support AIOps capabilities and CI/CD pipelines for AI agent deployment, versioning, and monitoring
• Ensure AI agent solutions are scalable, secure, observable, and production-ready

Requirements

• 8+ years of overall software engineering experience, with a strong focus on AI/ML systems in recent years
• Hands-on production experience with LangChain — including chains, agents, tools, retrievers, memory modules, and prompt templates
• Hands-on production experience with LangGraph — including stateful graph construction, conditional edges, checkpointing, human-in-the-loop interrupts, and multi-agent graph topologies
• Demonstrated experience designing and deploying multi-agent systems — including orchestrator/worker patterns, agent-to-agent communication, task delegation, and shared state management
• Experience implementing guardrails, ReAct patterns, and chain-of-thought reasoning within agentic pipelines
• Strong understanding of agent memory architectures (in-context, vector-store-backed, episodic) and tool-use patterns (function calling, MCP, LangChain tool wrappers)
• Familiarity with LangSmith or equivalent observability/tracing platforms for debugging and monitoring agent behaviour in production
• Strong Python engineering skills including async programming, APIs, and microservices
• Experience with AIOps and CI/CD pipeline development for AI agent deployment and lifecycle management
• Experience with Snowflake, Databricks, and Lakehouse architectures as grounding and tool-use data sources for agents
• Strong understanding of scalable distributed systems and cloud-native application development
• Strong communication and cross-functional collaboration skills