Senior Python / LangChain AI Agent Developer

Company DescriptionI&G serves as a trusted growth partner, specializing in scalable technology solutions and success-driven outcomes. Positioned as a Nearshore Delivery Hub, I&G operates at the strategic intersection of EMEA, enabling innovation and operational excellence. With a commitment to delivering cutting-edge solutions, I&G empowers businesses to achieve their goals with efficiency and precision.Role SummaryWe are 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.RAG and Knowledge-Based Systems·      Build and maintain Retrieval-Augmented Generation pipelines that connect AI agents with company knowledge and documents.·      Work with internal documents, service descriptions, policies, client information, proposals, reports, sales materials, and operational playbooks.·      Implement document ingestion, parsing, chunking, embeddings, vector indexing, metadata filtering, and retrieval logic.·      Improve response accuracy by grounding AI outputs in verified internal knowledge sources.·      Evaluate retrieval quality and reduce hallucinations through testing, validation, and prompt improvement.Backend Development and API Integrations·      Develop backend services and APIs that enable AI agents to interact with internal and external systems.·      Integrate AI agents with CRM platforms, email systems, calendars, spreadsheets, databases, document repositories, invoicing tools, webhooks, and internal dashboards.·      Build and maintain REST APIs using frameworks such as FastAPI or Flask.·      Implement secure authentication, authorization, error handling, logging, rate limiting, and retry mechanisms.·      Ensure that integrations are clean, maintainable, scalable, and properly documented.Testing, Evaluation, and Monitoring·      Write unit tests, integration tests, and workflow tests for AI agents and backend services.·      Evaluate prompt performance, agent behavior, retrieval accuracy, and output quality.·      Use observability and tracing tools such as LangSmith or similar platforms to debug and monitor agent workflows.·      Track latency, cost, error rates, model performance, and user feedback.·      Continuously improve agent reliability, response quality, system performance, and cost efficiency.Security and Governance·      Apply secure software development practices across AI and backend systems.·      Implement appropriate controls for data privacy, access control, secrets management, and safe tool execution.·      Identify and mitigate risks such as prompt injection, data leakage, hallucinations, excessive permissions, and unsafe automated actions.·      Ensure sensitive actions include validation, logging, and human approval where required.·      Maintain clear documentation for AI workflows, system architecture, APIs, and deployment processes.Required 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.Preferred Qualifications·      Hands-on experience with LangGraph for agent orchestration, state management, memory, checkpoints, and multi-step workflows.·      Experience delivering production AI agents or enterprise AI automation solutions, not only proof-of-concept applications.·      Experience with LangSmith or similar tools for observability, tracing, debugging, and evaluation.·      Experience in business automation, sales operations, marketing automation, HR automation, finance automation, consulting, or client-service environments.·      Experience integrating AI agents with CRM platforms, Microsoft 365, Google Workspace, Slack, Teams, accounting systems, invoicing tools, or internal business applications.Example Business Use CasesThe Senior Python / LangChain AI Agent Developer may work on AI agents that support the following workflows:·      Client follow-up and communication support.·      Document analysis and internal knowledge retrieval.·      Sales, marketing, HR, finance, and operational reporting.·      Invoice preparation and workflow support.·      Internal AI assistants for business teams and management.Ideal Candidate ProfileThe ideal candidate is a hands-on AI and backend engineer with strong Python skills and practical experience building LangChain-based applications. They understand how to design AI agents that solve real business problems, integrate with existing systems, and operate reliably in production.Key Success Factors·      Ability to build reliable AI agents using Python and LangChain.·      Strong understanding of RAG pipelines, vector databases, LLM APIs, and backend services.·      Ability to convert business requirements into automated AI workflows.·      Strong focus on security, testing, observability, maintainability, and production readiness.·      Experience integrating AI systems with business tools, APIs, databases, and document repositories.·      Strong problem-solving skills and ability to work with both technical and non-technical teams.Education·      Bachelor’s degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, Information Systems, or a related field is preferred.Application Requirements·      Updated CV or resume.·      Examples of relevant Python, LangChain, RAG, AI agent, or backend automation projects, where confidentiality allows.·      Overview of experience with LLM APIs, vector databases, API integrations, and deployment.·      Availability and salary expectations.