Job Summary

We are seeking an experienced GenAI Senior Manager with 12+ years of overall experience, including significant hands-on and leadership expertise in Generative AI, Agentic AI, RAG architectures, Conversational AI, AI agents, Copilot solutions, enterprise AI platforms, and AI governance. The ideal candidate will lead the strategy, architecture, delivery, governance, and enterprise-wide scaling of AI solutions while managing multi-disciplinary teams of AI engineers, data scientists, solution architects, platform engineers, and business stakeholders.

The role requires a strong blend of technical leadership, enterprise solution architecture, AI strategy, program delivery, stakeholder management, people leadership, quality governance, and business value realization to drive large-scale adoption of GenAI capabilities. The candidate should have proven experience delivering AI-powered solutions such as RAG applications, AI agents, copilots, enterprise knowledge management platforms, AI automation workflows, multi-agent systems, intelligent search, and AI-enabled business transformation programs, while ensuring Responsible AI, quality, security, compliance, scalability, and operational excellence standards are met.

The individual will lead enterprise AI quality, validation, observability, and governance initiatives to ensure the reliability, security, performance, scalability, and business effectiveness of AI and GenAI solutions while partnering with senior technical, business, architecture, security, compliance, and executive stakeholders.

Significant: Hands-on experience delivering AI solutions in enterprise or production environments is required. Certifications, personal projects, and demos alone are not sufficient.

Your key responsibilities

- Lead the strategy, design, development, and delivery of enterprise AI, GenAI, Agentic AI, Conversational AI, Copilot, and RAG-based solutions aligned with business and technology objectives.
- Define enterprise AI strategy, roadmaps, governance frameworks, reference architectures, operating models, delivery standards, and capability development plans to enable scalable AI adoption across the organization.
- Drive the architecture, implementation, and industrialization of AI platforms, copilots, multi-agent systems, conversational AI solutions, intelligent automation workflows, enterprise search, and knowledge management solutions.




- Collaborate with senior business stakeholders and executive sponsors to identify high-value AI use cases, prioritize initiatives, establish success metrics, and realize measurable business value.
- Oversee the end-to-end AI solution lifecycle, including use case discovery, feasibility assessment, solution architecture, data readiness, development, testing, deployment, monitoring, adoption, optimization, and continuous improvement.
- Lead, mentor, and manage cross-functional teams of AI engineers, data scientists, architects, developers, platform engineers, and business analysts, fostering innovation, engineering excellence, and delivery discipline.
- Establish and enforce best practices for Responsible AI, AI security, privacy, compliance, risk management, explainability, model governance, and ethical AI adoption across enterprise AI implementations.
- Drive adoption of enterprise AI capabilities leveraging Azure AI Foundry, Azure OpenAI, OpenAI, Databricks, Microsoft Copilot ecosystem, vector databases, enterprise search platforms, and modern AI orchestration frameworks.
- Ensure AI solutions are scalable, reliable, secure, cost-optimized, observable, maintainable, and aligned with enterprise architecture, cloud, data, security, and governance standards.
- Partner with engineering, data, security, risk, and business teams to define evaluation frameworks, quality benchmarks, guardrails, observability standards, and performance metrics for AI systems.
- Guide the implementation of RAG architectures, embedding strategies, retrieval pipelines, vector databases, knowledge ingestion frameworks, grounding mechanisms, and agentic workflows to enhance productivity and decision-making.
- Manage stakeholder communication, executive reporting, program governance, budgeting, resource planning, risk management, vendor coordination, delivery assurance, and benefits realization for AI programs.
- Stay current with emerging trends in GenAI, Agentic AI, multimodal AI, AI agents, autonomous workflows, LLMOps, AI governance, and enterprise AI platforms to drive innovation and competitive advantage.




- Contribute to practice development through reusable accelerators, reference architectures, estimation models, delivery playbooks, solution frameworks, thought leadership, and capability building initiatives.
- Drive AI solution quality through structured validation, model evaluation, regression testing, hallucination assessment, prompt quality reviews, security assessments, and production readiness checks.
- Support pre-sales, solution shaping, client conversations, capability presentations, proposal inputs, and executive-level AI transformation discussions as required.

Skills and Competencies

Professional Experience

- 12+ years of overall experience, including 6+ years leading AI/ML, GenAI, data, analytics, automation, or digital transformation initiatives in enterprise environments.
- Proven experience delivering, operationalizing, and scaling production-grade AI, GenAI, Agentic AI, Copilot, Conversational AI, and RAG-based solutions from concept through deployment, monitoring, adoption, and business value realization.
- Strong understanding of enterprise AI architecture and hands-on exposure to multiple areas, including:
- LLM and GenAI application development
- RAG architectures and enterprise knowledge management solutions
- Agentic AI, AI agents, and multi-agent systems
- Conversational AI, chatbot, virtual assistant, and Copilot solutions
- Prompt engineering, prompt optimization, and prompt lifecycle management
- AI evaluation, model validation, safety testing, guardrails, and Responsible AI
- AI governance, risk management, compliance, privacy, and security controls
- AI platform engineering and cloud-native AI deployments
- MLOps / LLMOps and operationalization of AI solutions
- Data engineering, vector databases, embeddings, semantic search, and retrieval pipelines
- AI observability, monitoring, cost optimization, performance tuning, and production support
- Integration of AI solutions with enterprise applications, APIs, workflows, and business processes
- Demonstrated ability to lead large cross-functional teams of AI engineers, architects, data scientists, developers, platform engineers, testers, and business analysts across multiple workstreams.

Disclaimer : This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 EY-GDS Consulting-AIA-Gen AI Senior Manager (Kolkata)
🏢 EY
📍 Kolkata