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AI Practice Head

AI Practice Head

BPMLinksChennai, IN
6 days ago
Job description

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
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