Job Description :

Role : AI / GenAI Delivery Manager

Experience : 7+Years

Location : Bhopal

We are looking for an experienced AI / GenAI Delivery Manager to lead enterprise-scale AI transformation initiatives involving Generative AI, LLMs, RAG, AI Agents, and intelligent automation solutions.

Key Responsibilities :

- Lead end-to-end delivery of AI/ML and GenAI programs.

- Design and oversee scalable AI architectures involving LLMs, RAG, AI Agents, Knowledge Bases, APIs, and enterprise integrations.

- Drive AI solution deployment, adoption, governance, and measurable business outcomes.

- Integrate AI solutions with ERP, CRM, BI tools, enterprise applications, and document repositories.

- Establish MLOps/LLMOps practices, CI/CD pipelines, and AI-enabled engineering workflows.

- Ensure security, access control, compliance, and responsible AI practices.

- Collaborate with business stakeholders, architects, engineering teams, and customers to deliver enterprise AI solutions.

Required Experience & Skills :

- 10+ years of experience in software delivery, solution architecture, AI/ML, or enterprise technology leadership.

- Proven experience designing and delivering end-to-end AI/GenAI solutions.

- Strong understanding of AI lifecycle, enterprise AI architecture, and production deployments.

- Hands-on experience with LLMs, RAG, AI Agents/Agentic AI, Vector Databases, and Prompt Engineering.

- Experience integrating AI systems with ERP, CRM, BI tools, APIs, enterprise applications, and document repositories.

- Exposure to OpenAI, Azure OpenAI, LangChain, LangGraph, LlamaIndex, or similar AI frameworks.

- Experience with MLOps, LLMOps, GitHub, CI/CD, DevOps, and cloud platforms (Azure/AWS/GCP).

- Experience implementing AI governance, security controls, guardrails, and role-based access mechanisms.

- Proven experience deploying AI solutions for enterprise users at scale.

Candidates should be able to demonstrate :

- AI solutions they have designed and delivered end-to-end, including architecture and key components involved.

- RAG-based solutions delivered and the business use cases addressed.

- AI Agent / Agentic AI / MCP-based solutions implemented and their practical applications.

- Integration of AI systems with enterprise applications, APIs, analytics platforms, and business workflows.

- Enterprise-scale AI deployments, user adoption, and business impact achieved.

- Security, governance, compliance, and access-control mechanisms implemented in AI systems.

- Experience designing solutions involving data pipelines, AI models, APIs, applications, dashboards, and analytics platforms.