Artificial Intelligence Engineer

Skills

AWS/Azure/GCP ,Fast API ,Gen AI tools Generative AI (LLMs RAG Fine-tuning), LangChain / LangGraph / LlamaIndex / CrewAI / AutoGen , Python Vector Databases (e.g. Pinecone)

Project Highlights

You will work on cutting-edge AI solutions focused on Generative AI, Retrieval- Augmented Generation (RAG), and Agentic AI.

The role involves building intelligent applications using Large Language Models (LLMs), knowledge graphs, and modern AI frameworks to deliver scalable, production-ready solutions for enterprise use cases.

Roles and Responsibilities

• Develop and optimize REST APIs and backend services using Python and FastAPI.
• Design, develop, and deploy Generative AI applications using Large Language Models (LLMs) such as GPT, Claude, Llama, and Gemini.
• Build end-to-end Retrieval-Augmented Generation (RAG) pipelines using vector databases, embeddings, hybrid search, and re-ranking techniques.
• Develop Agentic AI solutions using frameworks like LangChain, LangGraph, CrewAI, AutoGen, or LlamaIndex.
• Integrate Knowledge Graphs (Neo4j/GraphRAG) with AI applications to improve contextual reasoning and response accuracy.
• Deploy AI solutions on AWS, Azure, or GCP using Docker and CI/CD pipelines, ensuring scalability and reliability.
• Implement Responsible AI practices, including guardrails for hallucination, prompt injection, bias mitigation, and data security.
• Collaborate with cross-functional teams to gather requirements, design AI solutions, troubleshoot issues, and deliver production-ready applications.

Requirements

• 4–6 years of overall software development or AI/ML engineering experience with hands-on Generative AI project exposure.
• Strong proficiency in Python and frameworks such as FastAPI, PyTorch, or TensorFlow.
• Experience building Generative AI applications using LLMs (GPT, Claude, Llama, Gemini, Mistral, etc.).
• Hands-on experience with RAG pipelines, embeddings, vector databases (Pinecone, Chroma, FAISS, Weaviate, Milvus, pgvector), and prompt engineering.
• Experience with Agentic AI frameworks such as LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, or LlamaIndex.
• Practical knowledge of Neo4j, Graph Databases, GraphRAG, and Cypher queries.
• Experience with cloud platforms (AWS, Azure, or GCP), Docker, Git, and CI/CD pipelines.
• Familiarity with SQL/NoSQL databases, ETL processes, and REST APIs. Strong analytical, debugging, and problem-solving skills with excellent communication abilities.

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field.