Description

Job Summary

We are seeking an experienced GenAI Architect with 10+ years of overall experience to design, architect, and lead the delivery of enterprise-grade Generative AI solutions. The ideal candidate will have strong expertise in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI workflows, and cloud-based AI architectures.

The candidate will be responsible for translating business requirements into scalable, secure, and production-ready AI solutions while collaborating closely with business stakeholders, data scientists, and engineering teams.

Key Responsibilities

Design and architect enterprise-grade Generative AI solutions leveraging LLMs, RAG pipelines, and agentic AI frameworks.

Lead the development and implementation of scalable AI applications and intelligent automation solutions.

Architect end-to-end RAG solutions, including document ingestion, chunking, embedding generation, vector databases, retrieval, and response generation.

Design and implement agentic workflows using tools, APIs, orchestration frameworks, and multi-agent architectures.

Evaluate and integrate LLM models from providers such as OpenAI, Anthropic, Google, and open-source ecosystems.

Leverage cloud platforms such as AWS, Azure, or GCP to build and deploy enterprise AI solutions.

Establish best practices for prompt engineering, context management, model evaluation, performance optimization, and LLM observability.

Ensure AI solutions meet enterprise requirements for security, scalability, governance, compliance, and responsible AI.

Collaborate with business stakeholders and technical teams to identify AI opportunities and translate business use cases into technical architectures.

Provide technical leadership, architecture guidance, and mentorship to engineering teams throughout the development lifecycle.

Required Technical Skills

10+ years of overall experience in software engineering, solution architecture, AI/ML, or related technology domains.

Strong hands-on experience designing and implementing Generative AI and LLM-based applications.

Extensive experience with RAG architecture, vector databases, embeddings, semantic search, and knowledge retrieval systems.

Strong understanding of Agentic AI, AI agents, multi-agent systems, and tool/function calling.

Experience with frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, or similar.

Proficiency in Python and experience developing enterprise-grade applications and APIs.

Experience working with LLM providers such as OpenAI, Azure OpenAI, Anthropic, Gemini, or open-source LLMs.

Strong experience with at least one major cloud platform: AWS, Microsoft Azure, or Google Cloud Platform.

Knowledge of vector databases such as Pinecone, Azure AI Search, FAISS, Weaviate, Milvus, or Chroma.

Understanding of LLMOps, model deployment, monitoring, evaluation, and optimization.

Experience with enterprise security, data privacy, access controls, and responsible AI practices.

Preferred Qualifications

Experience architecting and delivering enterprise-scale GenAI solutions from concept to production.

Knowledge of Kubernetes, Docker, microservices, and CI/CD pipelines.

Experience with fine-tuning, model customization, and prompt optimization.

Familiarity with AI governance, guardrails, hallucination mitigation, and responsible AI implementation.

Excellent communication, stakeholder management, and technical leadership skills

Key Skills

Education

Any Graduate