AI Practice Head

Key Responsibilities : -

Practice Leadership & Strategy

Define and execute the AI / ML modernization practice roadmap, with a strong focus on GenAI, Agentic AI capabilities and Cognitive Automation.

Build and Develop a Center of Excellence (CoE) for AI-driven modernization and lead a high-performing team of AI engineers, data scientists, and modernization specialists.

Establish delivery models, reusable frameworks, and accelerators for AI-enabled modernization.

Collaborate with executive management, advisors, and clients to translate business challenges into AI modernization solutions.

Identify new business opportunities, partnerships, and go-to-market strategies across industries.

Drive thought leadership through whitepapers, webinars, and client advisory sessions.

AI / ML & GenAI Modernization

Architect and oversee AI-powered modernization programs including legacy-to-cloud transformation, API enablement, and LLM-driven replatforming.

Implement Generative AI copilots and autonomous agents that enhance developer productivity, testing, and cloud refactoring.

Design AI pipelines for code analysis, application refactoring, dependency graphing, and migration planning.

Integrate LLMs (OpenAI, Anthropic, Mistral, Azure OpenAI) with contextual knowledge retrieval (RAG, GraphRAG).

Technical Leadership

Architect modernization roadmaps using LLMs, multi-agent orchestration frameworks, and knowledge-driven AI pipelines.

Lead solution design for legacy-to-modern transitions using AI-driven code refactoring, intelligent process automation, and cloud-native AI microservices.

Oversee development of reusable AI accelerators, frameworks, and reference architectures.

Evaluate and integrate emerging GenAI tools, vector databases, agent frameworks (LangChain, AutoGen, CrewAI, etc.), and orchestration layers.

Agentic AI & Cloud Transformation

Lead the design and deployment of AI Agents that autonomously analyze, refactor, test, and deploy applications across multi-cloud ecosystems.

Develop agent workflows for cost-performance optimization, code modernization, and cloud-native adoption.

Integrate modernization frameworks with Neo4j, Pinecone, Aurora PostgreSQL, and other Smart Spend AI knowledge infrastructure.

Practice Building

Build and mentor a multidisciplinary AI / ML team of data scientists, ML engineers, solution architects, and GenAI specialists.

Establish delivery standards, best practices, and governance models for scalable AI deployments.

Collaborate with cross-functional leaders (cloud, data, DevOps, DevSecOps, security) to integrate AI capabilities across modernization programs.

Client Engagement & Delivery

Partner with enterprise customers to understand modernization pain points and design AI-led transformation journeys, AI-strategies, and deliver tangible value

Lead PoCs, pilots, and enterprise-scale rollouts that demonstrate ROI through AI automation and intelligence augmentation.

Build reference architectures, success stories, and thought leadership assets that position the company as an innovation leader.

Serve as a trusted advisor for C-suite leaders on AI strategy, platform transformation, and innovation adoption.

Lead pre-sales and consulting engagements for AI / ML modernization initiatives.

Innovation & Governance

Drive internal R&D initiatives to continuously improve GenAI-driven modernization accuracy and reliability.

Define Responsible AI and Policy-as-Code frameworks ensuring safety, explainability, and compliance.

Collaborate with product management to turn modernization insights into productized offerings and automation tools.

Required Qualifications : -

Bachelor’s or Master’s degree in Computer Science, AI / ML, or related fields; PhD preferred.

15 to 18 years of experience in technology consulting or product engineering, with at least 5 years in AI / ML practice leadership.

Proven success in modernizing enterprise applications using AI / ML, cloud-native architectures, and microservices .

Deep expertise in Generative AI (LLMs, Transformers, RAG, Multi-Agent Systems) and emerging Agentic AI frameworks .

Proven expertise in :

AI / ML architecture, LLMs, RAG / GraphRAG pipelines, and agentic workflows.

Cloud ecosystems (AWS, Azure, GCP) and modernization tools (Azure Migrate, AWS Refactor Spaces, etc.).

Python, Node.js, or Java with hands-on experience in AI libraries (LangChain, HuggingFace, PyTorch, TensorFlow).

Knowledge graphs, vector search, and orchestration frameworks.

Hands-on understanding of cloud AI ecosystems (AWS Sagemaker, Azure AI, GCP Vertex AI) .

Strong experience with MLOps, LLMOps, and AI governance .

Excellent stakeholder management, business acumen, and communication skills.

Preferred Qualifications : -

Experience serving clients in regulated industries (healthcare, finance, public sector)

Strong commercial acumen with experience in pre-sales, solutioning, and deal structuring

MBA or advanced degree in Computer Science, Engineering, or Technology Management