data engineering , Data Analytics, MLops, Data Governance, agentic AI frameworks, AI ML solutions, enterprise data architecture, data platform delivery, Ai, onshore-offshore delivery model, Analytics, master data and reference data platforms, MDM, data lakehouse architecture, cloud-native platforms, semantic and ontology layers

Jenkins, Devops, Git, Docker, Kubernetes, Python, AgenticAI frameworks, R, AI ML engineering, CI CD tools

Apis, Enterprise Integration, eval-driven quality practices, event-driven systems, cloud platforms, engineering productivity, agent frameworks, agentic systems, A2A, SDLC tooling, AI LLM-powered applications, MCP

Machine Learning, Jax, Sql, Tensorflow, Pytorch, Gcp, Docker, Terraform, Spark, Azure, Kubernetes, AWS, Airflow, Generative AI, Ai, MLflow, CI CD, Kubeflow

state management , Java, Data Architecture, Orchestration, AWS, Data Quality, Python, agentic workflows, EKS, tool calling, retrieval, PII controls, backfills, embeddings, GenAI systems, LangChain, RAG, lineage, LangGraph, evaluation monitoring, lakehouse data platform, guardrails, metadata, Governance, idempotent pipelines, Indexing, retries

Data Governance, Ml, Data Lineage, Data Quality, Analytics, Product Management, Ai, data integrity, Data Lakehouse Architecture, Generative AI frameworks

Change management, Artificial Intelligence, AI benefit measurement, AI architecture, Process Optimization, AI tools and implementation approaches, AI adoption strategy, Stakeholder Management, AI governance frameworks

, Discovery practices, Communication, AI-assisted product development, Performance management, Requirements quality