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