About the job

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:-

- Bachelors or Masters 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