### About the Role We are looking for an experienced AI Engineer (FDE) who can design, architect, and develop AI-powered solutions for diverse business use cases. The ideal candidate should possess strong GenAI and AI/ML expertise, be capable of solutioning across multiple AI technology stacks, and have excellent business communication skills to engage with stakeholders and translate business requirements into scalable AI solutions. ### Key Responsibilities - Design, architect, and implement AI/GenAI solutions for enterprise business use cases. - Engage with business stakeholders to understand challenges and recommend AI-driven solutions. - Build end-to-end AI applications using modern AI frameworks and cloud platforms. - Evaluate and adapt to different AI technologies, models, and platforms based on business needs. - Develop scalable RAG (Retrieval-Augmented Generation), AI Agents, and LLM-based applications. - Integrate AI solutions with enterprise systems and APIs. - Optimize AI model performance, cost, scalability, and security. - Collaborate with cross-functional teams including Product, Engineering, Data Science, and Business teams. - Stay updated with the latest advancements in AI, GenAI, LLMs, AI Agents, and cloud AI services. - Prepare solution architecture documents, technical proposals, and client presentations. ### Required Skills - 4+ years of experience in AI/ML or Generative AI solution development. - Strong experience with LLMs (OpenAI, Claude, Gemini, Llama, Mistral, etc.). - Hands-on experience building RAG pipelines, AI Agents, Agentic AI, and prompt engineering. - Experience with AI frameworks such as: - LangChain - LangGraph - LlamaIndex - Semantic Kernel - CrewAI / AutoGen (preferred) - - Strong programming skills in Python. - Experience with vector databases such as Pinecone, ChromaDB, Milvus, Weaviate, or FAISS. - Experience with cloud AI platforms (Azure AI Foundry, Azure OpenAI, AWS Bedrock, Google Vertex AI, etc.). - Good understanding of MLOps, model deployment, monitoring, and AI governance. - Experience integrating AI solutions using REST APIs and microservices. - Familiarity with containerization (Docker, Kubernetes) is an advantage.