Generative AI Architect

Job Title: Generative AI Architect - Senior Manager Type of Engagement: Full-Time / Contract Location: Open / As per Client Responsibilities : Lead the implementation of enterprise Generative AI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Agentic AI architectures. Design and develop multimodal data ingestion pipelines for processing structured and unstructured enterprise data. Build, containerize, and deploy REST APIs and AI services using FastAPI, Flask, or Node.js. Develop and deploy cloud-native AI applications across AWS, Azure, and GCP using serverless platforms and Kubernetes. Integrate LLMs with enterprise applications using embeddings, vector databases, and semantic search capabilities. Build Agentic AI workflows using frameworks such as LangGraph, CrewAI, and AutoGen. Collaborate with Technical Leads, Data Scientists, Product Managers, and Architects to deliver scalable GenAI solutions. Own end-to-end technical delivery including sprint planning, architecture reviews, engineering execution, and production deployments. Provide technical leadership across multiple AI initiatives while mentoring engineering teams and ensuring best engineering practices. Work directly with client stakeholders to translate business problems into AI solution strategies and implementation plans. Prepare architecture diagrams, technical documentation, solution playbooks, presentations, and reusable implementation assets. Support Agile delivery using JIRA, Azure DevOps, and modern software development practices. Required Skills: 10–12+ years of experience in AI/ML, Software Engineering, Data Engineering, or Cloud Application Development. 2–3+ years of hands-on experience designing and implementing cloud-native AI applications. Strong expertise in Generative AI, Large Language Models (LLMs), Prompt Engineering, and Retrieval-Augmented Generation (RAG). Experience with Agentic AI frameworks such as LangGraph, CrewAI, AutoGen, or similar orchestration frameworks. Hands-on experience developing REST APIs using FastAPI, Flask, or Node.js. Strong Python programming skills for AI application development. Experience with Docker, Kubernetes, and containerized microservices. Experience implementing semantic search using embeddings and Vector Databases (Pinecone, Weaviate, Milvus, ChromaDB, FAISS, Qdrant, or similar). Strong knowledge of cloud platforms including AWS, Microsoft Azure, and Google Cloud Platform (GCP). Experience with serverless computing, cloud storage, NoSQL databases, networking, and distributed systems. Proven experience leading end-to-end technical delivery and managing cross-functional engineering teams. Strong client-facing communication, solution architecture, and stakeholder management skills. Preferred Skills: Experience with LangChain, LlamaIndex, Semantic Kernel, or Haystack. Experience with Azure OpenAI, Amazon Bedrock, Vertex AI, Azure AI Foundry, or SageMaker. Knowledge of LLMOps/MLOps tools such as MLflow, LangSmith, Kubeflow, or Arize Phoenix. Experience implementing AI governance, Responsible AI, security, and compliance best practices. Experience with CI/CD pipelines, GitHub Actions, Jenkins, Terraform, or Infrastructure as Code (IaC). Familiarity with Agile/Scrum methodologies, JIRA, and Azure DevOps. Additional Information: Leadership role focused on designing, architecting, and delivering enterprise-scale Generative AI solutions. Strong hands-on experience with LLMs, RAG, Agentic AI, Cloud Platforms, APIs, Docker, Kubernetes, and AI engineering is required. Candidates should have experience leading engineering teams, working with enterprise clients, and delivering production-ready AI applications. Experience building scalable AI Assistants, Enterprise Search, AI Copilots, Knowledge Management platforms, and intelligent automation solutions is highly preferred. Show more Show less