This role focuses on designing, building, and guiding the implementation of Generative AI solutions for enterprise use cases. The ideal candidate will possess strong hands-on experience and architectural expertise in areas such as Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), vector databases, and agentic AI frameworks.

Expertise in cloud-native deployments and CI/CD automation is essential. The position involves close collaboration with business stakeholders, data/ML teams, and engineering teams to deliver scalable, secure, and production-ready GenAI platforms and applications.

Key responsibilities include architecting and designing end-to-end GenAI solutions, encompassing RAG pipelines, agentic workflows, and multimodal use cases. You will translate complex business problems into actionable GenAI-driven use cases, create solution blueprints, and define implementation roadmaps.

Further duties involve designing and implementing effective retrieval systems using embeddings, chunking strategies, metadata filters, reranking, and evaluation metrics. You will select and integrate vector databases, optimizing indexing, retrieval performance, and relevance tuning. Building agentic systems with frameworks like LangChain and LangGraph, defining cloud architecture patterns for scalable and secure GenAI deployments, and driving productionization through CI/CD pipelines and containerization are also critical aspects of this role. Ensuring responsible AI practices in security, governance, privacy, compliance, and monitoring, along with providing technical leadership and mentoring to engineering teams, will be paramount.

A strong foundation in GenAI algorithms and LLM concepts is required, including prompting, fine-tuning versus RAG, embeddings, context windows, token limits, and hallucination control. Proven experience in designing enterprise GenAI use cases such as document Q&A, copilots, summarization, search, workflow automation, customer support, and knowledge assistants is essential.

Candidates must have a hands-on understanding of vector databases and similarity search concepts, including embeddings, indexing, ANN search, hybrid search, and metadata filtering. Familiarity with tools like Pinecone, FAISS, Weaviate, Chroma, Milvus, Azure AI Search, or Elastic (vector) is expected. Strong working knowledge of agentic frameworks like LangChain, LangGraph, and MCP, covering tool calling, memory, planning, multi-agent workflows, and guardrails, is also a must.

Experience with cloud services and architecture for GenAI workloads, including compute, networking, storage, IAM/security, and logging/monitoring, is necessary. Knowledge of cloud components supporting AI/ML solutions, with a preference for managed services, is also required.

Proficiency in implementing CI/CD pipelines for GenAI applications and services is crucial. A strong understanding of deployment patterns, including containerization (Docker), orchestration (Kubernetes), API deployment, and model endpoint integration, is expected. Familiarity with DevOps/MLOps practices such as testing, observability, rollback, scaling, and cost controls is also important.