<p><strong>Job Title: Generative AI Python Developer</strong></p><p><strong>Job Descriptio</strong>n</p><p>We are seeking an experienced <strong>Generative AI Python Developer</strong> to design, develop, and deploy scalable AI-powered applications using <strong>Python, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Agentic AI frameworks</strong>. The ideal candidate will have strong Python backend development experience and hands-on expertise in building production-ready GenAI solutions, AI agents, and LLM-powered applications.</p><p><strong>Key Responsibilities</strong></p><ul><li>Design, develop, and deploy <strong>Generative AI and LLM-based applications</strong> using Python.</li><li>Build end-to-end <strong>Retrieval-Augmented Generation (RAG)</strong> pipelines for enterprise use cases.</li><li>Develop scalable backend services and <strong>REST APIs</strong> using <strong>FastAPI, Flask, or Django</strong>.</li><li>Integrate LLMs such as <strong>GPT, Claude, Gemini, Llama, and Mistral</strong> into enterprise applications.</li><li>Build AI workflows and applications using <strong>LangChain, LangGraph, LlamaIndex, AutoGen, or CrewAI</strong>.</li><li>Design and implement <strong>Agentic AI and multi-agent workflows</strong>, including tool calling and workflow orchestration.</li><li>Implement <strong>prompt engineering</strong>, structured outputs, function calling, context management, and prompt optimization.</li><li>Develop document ingestion, chunking, embedding, semantic search, retrieval, and reranking pipelines.</li><li>Work with <strong>vector databases</strong> such as Pinecone, FAISS, Chroma, Weaviate, Milvus, or pgvector.</li><li>Integrate GenAI applications with enterprise databases, APIs, data platforms, and knowledge repositories.</li><li>Evaluate and improve LLM application quality, including <strong>accuracy, relevance, hallucination reduction, latency, and cost optimization</strong>.</li><li>Implement LLM observability, tracing, evaluation, logging, monitoring, and guardrails.</li><li>Deploy AI applications and microservices using <strong>Docker, Kubernetes, and CI/CD pipelines</strong>.</li><li>Build and deploy cloud-based AI solutions using <strong>AWS, Azure, or GCP</strong>.</li><li>Apply security, data privacy, Responsible AI, and governance best practices to production GenAI applications.</li><li>Collaborate with Data Scientists, ML Engineers, Data Engineers, Software Engineers, and Product teams to deliver scalable AI solutions.</li></ul><p><strong>Required Technical Skills</strong></p><ul><li>Strong programming experience with <strong>Python</strong></li><li>Generative AI and <strong>Large Language Models (LLMs)</strong></li><li><strong>RAG (Retrieval-Augmented Generation)</strong></li><li><strong>Agentic AI / AI Agents</strong></li><li><strong>LangChain, LangGraph, LlamaIndex, AutoGen, or CrewAI</strong></li><li><strong>FastAPI, Flask, or Django</strong></li><li>Prompt Engineering and LLM API Integration</li><li>Embeddings, Semantic Search, Chunking, and Reranking</li><li>Vector Databases: <strong>Pinecone, FAISS, Chroma, Weaviate, Milvus, or pgvector</strong></li><li>REST APIs and Microservices</li><li>SQL and NoSQL Databases</li><li>Git, Docker, Kubernetes, and CI/CD</li><li>Cloud Platforms: <strong>AWS / Azure / GCP</strong></li><li>Experience with LLM providers and platforms such as <strong>OpenAI APIs, Azure OpenAI, AWS Bedrock, or Google Vertex AI</strong></li></ul><p><strong>Preferred Skills</strong></p><ul><li>Experience building <strong>production-grade GenAI or RAG applications</strong>.</li><li>Hands-on experience with <strong>multi-agent systems and Agentic AI workflows</strong>.</li><li>Knowledge of <strong>Model Context Protocol (MCP)</strong> and tool-based AI integrations.</li><li>Experience with LLM evaluation and observability frameworks.</li><li>Understanding of <strong>Machine Learning, NLP, Transformers, and Deep Learning</strong>.</li><li>Experience with model fine-tuning techniques such as <strong>LoRA/PEFT</strong> is a plus.</li><li>Strong understanding of scalable software architecture and AI application security.</li></ul><p><strong>Qualifications</strong></p><ul><li>Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field.</li><li>Strong hands-on experience in <strong>Python development and Generative AI application development</strong>.</li><li>Proven experience delivering end-to-end AI/LLM solutions in production environments.</li></ul><p><br></p>
