Generative AI Engineer

About the RoleWe are looking for a hands-on AI Engineer with at least 1 year of experience to build, develop, and integrate AI-powered applications using Python, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Agentic AI frameworks.The ideal candidate should have practical experience with LangChain, LangGraph, LLM APIs, backend development, and AI agent workflows.This role focuses on applied AI engineering, building real-world AI solutions, integrating intelligent capabilities into applications, and improving the reliability, scalability, and performance of AI-powered systems.Key Responsibilities Develop Python-based applications using LLMs and Generative AI. Build AI-powered solutions using LangChain and LangGraph. Integrate OpenAI, Anthropic Claude, Gemini, and other LLM APIs. Develop backend services using FastAPI or similar frameworks.Design and implement Retrieval-Augmented Generation (RAG) pipelines.Work with document processing, chunking, embeddings, and semantic search.Integrate vector databases such as Chroma, FAISS, Pinecone, or pgvector.Optimize retrieval relevance, accuracy, and response quality.Build AI agents capable of tool calling, API integration, and multi-step task execution.Develop stateful agent workflows and orchestration using LangGraph.Integrate agents with external APIs, databases, and business applications.Implement prompt engineering, context management, and agent memory.Design and develop REST APIs using Python and FastAPI.Integrate AI applications with databases and third-party services.Write clean, reusable, and maintainable code.Debug, troubleshoot, and optimize backend and AI applications.Evaluate LLM responses for accuracy, relevance, and consistency.Identify and minimize hallucinations and incorrect outputs.Optimize latency, token consumption, and API costs.Implement logging, retries, error handling, and monitoring.Collaborate with the team to improve application performance and reliability.Required Skills & QualificationExperience: Minimum 1 year of software development experience, preferably in Python and AI/LLM applications.Python: Strong understanding of Python, OOP, asynchronous programming basics, and API integration.Generative AI: Hands-on experience working with LLM APIs and building AI-powered applications.LangChain & LangGraph: Practical experience with LLM workflows, AI agents, and orchestration.RAG: Understanding of document processing, embeddings, chunking, retrieval, and vector search.Agentic AI: Knowledge of tool/function calling, agent workflows, and multi-step automation.Vector Databases: Experience with Chroma, FAISS, Pinecone, pgvector, or similar technologies.Backend Development: Experience with FastAPI, Flask, or similar frameworks and REST API development.Databases & Tools: Working knowledge of SQL, Git, debugging, and software development practices.Problem-Solving: Ability to independently develop AI features, troubleshoot issues, and collaborate effectively.Good to haveExperience with AWS services, including Lambda, S3, API Gateway, Bedrock, or EC2.Familiarity with Docker, CI/CD, and cloud deployment.Understanding of Model Context Protocol (MCP).Experience building multi-agent systems and implementing agent memory.Exposure to LLM evaluation, tracing, and observability platforms.Familiarity with hybrid search, reranking, and advanced RAG techniques.Experience integrating AI solutions into enterprise applications.Knowledge of prompt optimization, caching, and LLM cost management.Education BackgroundBachelor's degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, or a related field.Candidates with strong practical experience and relevant technical projects are also encouraged to apply.