AI Architect – GenAI & Agentic AI

Location: India – Mumbai | Pune | Gurugram

Job Summary

We are looking for a highly technical and hands-on AI Architect to design, build and lead the architecture of enterprise-grade AI, Generative AI and Agentic AI solutions. The ideal candidate will combine deep AI/ML expertise with strong software and solution architecture skills and the ability to take complex AI concepts from experimentation to scalable, production-ready systems. This is a hands-on architecture role requiring robust technical depth and current knowledge of modern AI frameworks.

Key Responsibilities

- AI Architecture & Solution Design: Design scalable, secure and production-ready architectures for AI, GenAI, Agentic AI and ML solutions across enterprise environments.
- Hands-On GenAI & Agentic AI: Design and develop LLM applications, agentic workflows, multi-agent systems, RAG pipelines, tool/function calling, memory and orchestration patterns. Build and evaluate working prototypes and production solutions.
- Technical Leadership: Provide technical direction to AI/ML Engineers, Data Scientists, Data Engineers and Software Engineers. Establish architecture standards, design principles and reusable AI components.
- LLM & Model Engineering: Work with foundation models and enterprise LLM platforms. Evaluate model selection, prompting, fine-tuning, PEFT/LoRA/QLoRA, quantization, inference and model optimization.
- RAG & Knowledge Architecture: Design enterprise RAG solutions using embeddings, vector databases, knowledge graphs, ontologies, retrieval strategies, grounding and context engineering.
- Production AI Engineering:



Drive AI solutions from POC to production, including APIs, scalability, observability, evaluation, security, reliability and performance optimization.
- Cloud & AI Platforms: Architect solutions using Azure, AWS or GCP and platforms such as Azure AI Foundry, AWS Bedrock, Vertex AI or equivalent.
- MLOps / LLMOps & Responsible AI: Design approaches for model/version management, evaluation, monitoring, CI/CD, LLMOps, governance, security and responsible AI.
- Technical Consulting: Translate business problems into AI use cases and present architecture decisions, trade-offs and technical roadmaps to senior stakeholders. Qualifications and Experience

Education

PhD in Artificial Intelligence, Machine Learning, Computer Science, Data Science, Statistics, Computational Science or a closely related technical discipline. PhD is mandatory.

- Experience: 15+ years in AI/ML, software engineering, data science, solution architecture or related technology roles, with significant experience in AI architecture and engineering.
- AI / GenAI Expertise: Deep, hands-on expertise in Generative AI, LLMs, Agentic AI, ML and AI application development. Strong understanding of transformer-based models, embeddings, context engineering and AI system design.
- Agentic AI:



Strong practical experience with agent orchestration, multi-agent systems, tool use, planning, memory and frameworks such as LangGraph, LangChain, CrewAI or equivalent.
- RAG & Enterprise AI: Strong experience with RAG architectures, vector search, knowledge graphs, ontologies, data pipelines, retrieval and evaluation frameworks.
- Programming & Engineering: Expert-level Python with strong software engineering fundamentals, APIs, distributed systems, data structures and integration patterns. Experience with SQL and modern data platforms.
- Cloud & AI Platforms: Strong hands-on experience with at least one of Azure, AWS or GCP and enterprise AI services such as Azure AI Foundry, AWS Bedrock or Vertex AI.
- Model Optimization: Practical understanding of fine-tuning, PEFT/LoRA/QLoRA, quantization, inference optimization and model evaluation.
- MLOps / LLMOps: Experience with production deployment, CI/CD, containers, Kubernetes, model monitoring, LLM evaluation, observability, security and governance.
- Architecture Leadership: Ability to own end-to-end technical architecture, make architecture trade-offs, mentor senior engineers and communicate complex technical concepts clearly.

Ideal Candidate Profile A research-oriented but highly practical AI technologist who is comfortable writing code, designing architectures, experimenting with models and frameworks, and taking AI systems into production. The candidate should demonstrate genuine hands-on depth rather than only high-level AI strategy, consulting or program management experience.

📌 Director of Artificial Intelligence (Mumbai)
🏢 eClerx
📍 Mumbai