AI Engineer - Forward Deployed Engineer

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.