Design, develop, and deploy AI-powered applications using Large Language Models (LLMs). | Build intelligent chatbots, AI assistants, copilots, and workflow automation solutions. | Develop and optimize Retrieval-Augmented Generation (RAG) pipelines using enterprise data sources and vector databases. | Design and implement AI Agents and Multi-Agent Systems capable of reasoning, planning, and autonomous task execution. | Build agentic workflows integrating external APIs, databases, enterprise applications, and third-party services. | Implement tool-calling frameworks and Model Context Protocol (MCP) integrations. | Develop scalable backend services and APIs for AI applications. | Evaluate, benchmark, monitor, and continuously improve AI system performance. | Collaborate with product, engineering, and business teams to deliver AI-driven solutions. | Stay up to date with the latest advancements in Generative AI, Agentic AI, LLMs, and emerging AI technologies. | Strong proficiency in Python with experience building scalable, production-grade applications. | Hands-on experience in Generative AI, LLMs, and AI-powered application development. | Experience working with foundation models such as GPT, Claude, Gemini, Llama, Mistral, DeepSeek, Qwen, or similar. | Strong understanding of Prompt Engineering, Function Calling, Tool Calling, Structured Outputs, Context Management, and LLM Evaluation. | Experience designing and building AI Agents, Agentic Workflows, and Multi-Agent Systems. | Hands-on experience with frameworks such as LangChain, LangGraph, CrewAI, AutoGen, OpenAI Agents SDK, LlamaIndex, PydanticAI, or similar. | Experience designing and implementing RAG pipelines and enterprise knowledge retrieval systems. | Strong understanding of embeddings, semantic search, hybrid search, reranking, and vector retrieval techniques. | Experience with Vector Databases such as Pinecone, Qdrant, Weaviate, ChromaDB, FAISS, or Milvus. | Experience building and deploying REST APIs using FastAPI, Flask, or similar frameworks. | Familiarity with Git, Docker, CI/CD practices, and cloud platforms such as AWS, Azure, or GCP. | Strong problem-solving, analytical, and communication skills. | Self-driven mindset with ownership and accountability. | Experience implementing and integrating Model Context Protocol (MCP). | Exposure to Agentic RAG, GraphRAG, Knowledge Graphs, and advanced retrieval architectures. | Experience deploying open-source LLMs using Ollama, vLLM, Hugging Face, TensorRT-LLM, or similar tools. | Exposure to LLMOps and MLOps platforms such as MLflow, LangSmith, Phoenix, Weights & Biases, or Arize. | Experience with AI evaluation frameworks, observability platforms, monitoring, and guardrails. | Knowledge of Kubernetes, distributed systems, and scalable AI infrastructure. | Experience building enterprise chatbots, AI copilots, workflow automation solutions, and autonomous AI systems. | Familiarity with multimodal AI applications involving text, image, audio, and document understanding. | Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, Information Technology, or a related field. | 1–3 years of relevant experience in AI/ML, Generative AI, LLM Engineering, or AI Application Development. | Strong understanding of software engineering best practices and system design principles. | Passion for Generative AI, Agentic AI, and emerging AI technologies. | Ability to rapidly learn and adopt new AI frameworks, tools, and methodologies. | Experience taking AI solutions from proof-of-concept to production deployment. | Strong collaboration and communication skills. | Ownership-driven mindset with a focus on building impactful AI products. | Work on cutting-edge Generative AI, Agentic AI, and Multi-Agent Systems. | Build production-grade AI products with real-world business impact. | Opportunity to work with the latest LLMs, MCP, RAG, Agentic AI, and AI infrastructure technologies. | Collaborative, innovation-driven environment with excellent learning and growth opportunities.