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.
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