We are Hiring Python LangChain AI Agent Developer

Role Summary

Our client is looking for a Senior Python / LangChain AI Agent Developer with 4–6 years of professional experience to design, develop, and deploy AI-powered agents and automation solutions for real business use cases.

This role is focused on building practical, production-ready AI agents rather than simple chatbot applications. The successful candidate will develop intelligent systems that can reason, use tools, retrieve knowledge, connect with APIs, automate business workflows, and support teams across areas such as sales, marketing, operations, business development, HR, finance, and client delivery.

The ideal candidate will have strong Python backend development skills, hands-on experience with LangChain, and practical exposure to LLM APIs, RAG pipelines, vector databases, workflow orchestration, API integrations, and secure deployment practices. Experience with LangGraph will be considered a strong advantage.

Key Responsibilities

• AI Agent Development
• Design, build, test, and deploy AI agents using Python, LangChain, and modern LLM frameworks.
• Develop autonomous and semi-autonomous agents capable of reasoning, tool usage, knowledge retrieval, API calling, and workflow execution.
• Build AI agents that support business processes such as lead generation, lead qualification, CRM updates, document analysis, proposal preparation, reporting, client follow-up, and operational support.
• Implement structured outputs, tool calling, memory, prompt templates, response validation, and business rules.
• Ensure that AI agents are reliable, secure, explainable, and suitable for production environments.

LangChain, LangGraph, and Workflow Orchestration

• Develop agent workflows using LangChain and, where applicable, LangGraph.
• Design multi-step workflows involving research, data extraction, decision support, document generation, and task automation.
• Implement human-in-the-loop approval steps for sensitive or business-critical actions.
• Support state management, memory, checkpoints, retries, fallback logic, and workflow monitoring.
• Collaborate with business stakeholders to convert operational requirements into automated AI workflows.

Qualifications

• 4–6 years of professional software engineering experience, with strong hands-on Python development experience.
• Practical experience building applications or automation workflows using LangChain.
• Experience integrating LLM APIs such as OpenAI, Anthropic, Azure OpenAI, Google Gemini, Mistral, Groq, or similar providers.
• Strong knowledge of Python backend development, including API development, asynchronous programming, testing, packaging, and clean code principles.
• Experience building RAG pipelines using embeddings, vector databases, document loaders, retrievers, metadata filters, and prompt engineering.
• Experience with backend frameworks such as FastAPI, Flask, or similar.
• Good understanding of REST APIs, webhooks, authentication, background jobs, logging, and database integrations.
• Practical experience with SQL databases and at least one vector database such as Pinecone, Weaviate, Qdrant, Chroma, pgvector, or similar.
• Experience with Docker, Git, CI/CD pipelines, cloud platforms, and production deployment practices.
• Understanding of AI safety topics such as hallucination reduction, prompt injection risks, access control, secure API usage, and responsible automation.
• Ability to translate business requirements into technical workflows and working software solutions.

Technical Skills

• Programming: Python, asynchronous programming, clean architecture, testing frameworks, package management.
• AI Frameworks: LangChain, LangGraph, LangSmith or similar tools.
• LLM Providers: OpenAI, Anthropic, Azure OpenAI, Google Gemini, Mistral, Groq, or similar.
• Agent Development: Tool calling, structured outputs, memory, prompt templates, ReAct-style reasoning, workflow orchestration, and human-in-the-loop flows.
• RAG: Embeddings, vector search, document ingestion, chunking, metadata filtering, retrieval evaluation, and grounding.
• Backend: FastAPI, Flask, REST APIs, webhooks, background jobs, authentication, error handling, and logging.
• Databases: PostgreSQL, MySQL, MongoDB, Redis, Pinecone, Weaviate, Qdrant, Chroma, pgvector, or similar.
• DevOps: Git, Docker, CI/CD, cloud deployment, environment management, monitoring, and production logging.
• Business Integrations: CRM systems, email APIs, calendar APIs, document repositories, spreadsheet automation, workflow tools, and SaaS integrations.

Education

• Bachelor’s degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, Information Systems, or a related field is preferred.
• Equivalent practical experience in AI engineering, backend development, or business automation will also be considered.