Headline

Senior Data & AI Leader | GenAI & Agentic AI Architect | Data Engineering | BI | ML/NLP | AI Platforms

About

I have spent nearly 15 years working across the data lifecycle — starting with SQL, data engineering, data modeling and BI, moving into advanced analytics, machine learning and NLP, and more recently into Generative AI, RAG and Agentic AI architecture. That journey has shaped how I look at AI today. For me, building an AI solution is not just about selecting an LLM or an agent framework. A production-grade AI system still depends on the fundamentals: reliable data pipelines, governed data, metadata, security, scalable architecture, evaluation, observability and clear business outcomes. In my current role, I lead cross-functional engineering teams delivering Data & AI solutions for global banking use cases across risk, regulatory reporting, finance and enterprise controls. My work spans solution architecture, technical leadership and hands-on engineering across Python, SQL, PySpark, cloud data platforms, ML, RAG and Agentic AI systems. I have worked on solutions covering: • Enterprise Agentic AI — orchestration, tool calling, MCP, multi-agent patterns, human-in-the-loop controls, guardrails and agent evaluation • Generative AI & RAG — embeddings, hybrid retrieval, reranking, enterprise knowledge assistants and grounded generation • Machine Learning & NLP — classification, prediction, anomaly detection, explainability, NLP and transformer-based solutions • Data Engineering — ingestion, ETL/ELT, BigQuery, Spark/PySpark, data quality, reconciliation, metadata, lineage and performance optimization • BI & Analytics — semantic layers, KPI frameworks, executive analytics and enterprise reporting using Qlik, Power BI, Tableau and SAP BO • Cloud & AI Platforms — strong GCP foundations with BigQuery, Dataflow, Cloud Run and Vertex AI/Gemini, along with Azure AI and working AWS exposure What interests me most now is the intersection of Data + Cloud + AI + Agentic systems. I believe the next generation of enterprise AI will not be built by treating AI as a separate layer. It will come from connecting trusted enterprise data, intelligent reasoning, tools, workflows and governed autonomy into one architecture. That is the space I am continuing to build in — combining years of enterprise data experience with hands-on AI engineering to design AI systems that are useful, scalable, secure and production-ready. Focus: Enterprise AI Architecture | GenAI | Agentic AI | RAG | GCP | Data Engineering | ML/NLP | AI Platforms | Data & AI Leadership

Location

Bengaluru

AI stack

claude code

claude

langchain

langgraph

mcp

openai

Work experience

Associate Director at HSBC, Jun 2024 - Present · 2 yrs 5 mos, Bengaluru, Karnataka, India, Leading enterprise Data, Analytics and AI initiatives within Global Banking, with responsibility spanning solution architecture, technical delivery, engineering governance and stakeholder management across risk, regulatory reporting, finance and control functions. My role combines hands-on technical depth with enterprise-level leadership, covering the full lifecycle from data ingestion and engineering through analytics, ML and modern Generative/Agentic AI solutions. Key areas of responsibility include: Lead a 17-member cross-functional team across AI engineering, data engineering, analytics and quality engineering. Architect enterprise GenAI and Agentic AI solutions using RAG, hybrid retrieval, tool calling, MCP, orchestration, human-in-the-loop controls and AI evaluation frameworks. Design solutions integrating structured enterprise data with unstructured knowledge, enabling agents to reason across policies, APIs, databases and analytical systems. Provide architecture direction across Python, SQL, PySpark, BigQuery, data pipelines, APIs, ML/NLP, cloud platforms and enterprise analytics. Drive data readiness for AI, including ingestion, transformation, data quality, reconciliation, metadata, lineage, semantic layers and governed data products. Establish production patterns for LLMOps/MLOps, evaluation, observability, model/prompt versioning, security, guardrails, cost monitoring and responsible AI. Lead architecture and design reviews while mentoring engineers and translating complex business requirements into scalable Data & AI solutions. Partner with global stakeholders across Business, Risk, Finance, Architecture, Cybersecurity and Platform Engineering to drive roadmaps, governance and production adoption. Current focus is on building the bridge between enterprise data platforms and intelligent agentic systems — enabling AI solutions that are grounded, explainable, governed and ready for real-world enterprise adoption.

Senior Manager Analytics at Accenture, Nov 2020 - Jun 2024 · 3 yrs 8 mos, Bengaluru, Karnataka, India, Led end-to-end AI, Machine Learning, NLP, Data Engineering and Analytics solutions for enterprise clients, combining hands-on technical delivery with architecture, team leadership and stakeholder management. My role covered the complete lifecycle from business problem discovery, data engineering and feature preparation through model development, evaluation, deployment, monitoring and business consumption. Key responsibilities and contributions: • Delivered ML solutions for financial risk prediction, prioritization and decision intelligence using Python, scikit-learn, XGBoost, LightGBM and SHAP-based explainability. • Built NLP and document intelligence solutions for classification, entity extraction, intent detection and semantic matching using TF-IDF/SVM, BERT, Transformers and PyTorch. • Designed GCP data engineering and analytics solutions using BigQuery, Dataflow, Cloud Storage, SQL and PySpark for scalable multi-source workloads. • Developed curated datasets, validation and reconciliation frameworks, semantic layers and governed KPI models for enterprise analytics. • Applied MLOps practices including model tracking, registry, automated testing, CI/CD, drift monitoring and controlled retraining. • Supported containerized REST/API deployments and integration of ML outputs into operational workflows and analytics applications. • Led architecture reviews, reusable engineering standards and mentoring across Python, ML, data engineering, cloud and production readiness. • Worked closely with business SMEs, architects and delivery teams to translate business requirements into scalable Data & AI solutions. This experience strengthened my ability to connect Data Engineering + Analytics + ML/NLP + Cloud + BI into a single enterprise solution lifecycle. Core areas: AI/ML | NLP | GCP | BigQuery | PySpark | Data Engineering | MLOps | BI & Analytics | Python | SQL | Technical Leadership

Deputy Manager- Delivery Lead at Vodafone, Nov 2017 - Nov 2020 · 3 yrs 1 mo, Led end-to-end AI/ML, Data Engineering and Analytics solutions for telecom network operations, service assurance and customer experience, combining hands-on technical delivery with team coordination and business stakeholder engagement. My role covered the complete lifecycle from data ingestion and profiling through feature engineering, model development, operational scoring, API integration, dashboards and production monitoring. Key responsibilities and contributions: • Delivered 10+ analytics and AI applications using OSS/NMS alarms, network KPIs, topology, asset, incident and operational data. • Built predictive models for network failure and service degradation, helping operations teams prioritize high-risk network elements before major impact. • Developed root-cause prediction and incident analytics by correlating alarm sequences, performance degradation, topology and historical incidents. • Designed NLP-based solutions for operations ticket classification and routing using TF-IDF/SVM, Word2Vec, BiLSTM and BERT-based approaches. • Worked extensively with Python, SQL, PySpark, Spark and machine-learning models including Random Forest and XGBoost. • Engineered time-window, alarm-frequency, KPI-deviation and topology-aware features from large operational datasets. • Integrated model outputs into Qlik Sense and Power BI dashboards, enabling NOC teams to consume predictions and reason codes directly within operational workflows. • Applied confidence thresholds, human fallback, model monitoring and feedback-driven retraining to improve production reliability. • Worked closely with network SMEs and operations teams to convert technical telemetry into actionable business and operational intelligence. Core areas: AI/ML | Predictive Analytics | NLP | PySpark | SQL | Telecom Analytics | Root Cause Analysis | Qlik Sense | Power BI | Data Engineering | Technical Leadership

Senior Data Analyst at HARMAN International, May 2016 - Nov 2017 · 1 yr 7 mos, Bengaluru, Karnataka, India, Served as the lead BI resource, owning end-to-end delivery of business intelligence and data-reporting requirements. Led and supported a team of BI professionals through work allocation, technical guidance, reviews, mentoring, and issue resolution. Developed project-management capabilities across estimation, delivery planning, milestone tracking, risk management, and stakeholder communication. Contributed to the design and delivery of GCP-based data pipelines for ingesting, transforming, and preparing data for analytics and reporting. Translated business requirements into scalable data solutions, reporting datasets, dashboards, and actionable insights. Coordinated with business stakeholders, data engineers, and technology teams to manage dependencies and deliver solutions within agreed timelines. Established quality checks and review processes to improve data accuracy, reporting reliability, and production readiness. Supported performance optimization and troubleshooting across data pipelines, SQL workloads, and BI reporting solutions. Promoted knowledge sharing and reusable development practices across the BI team. Received annual Best Performer recognition for technical contribution, delivery ownership, teamwork, and consistent performance.

Data Analyst at Quosphere, Jun 2012 - Apr 2016 · 3 yrs 11 mos, Mumbai Metropolitan Region, Deployed to Mahindra and Mahindra and L&T Finance worked on the reporting environment of multiple domains of M&M by using SQL and Qlikview platforms. Worked as a core SQL Developer supporting data and reporting requirements across loans, mortgages, and insurance portfolios. Designed, developed, and optimized complex SQL queries for operational reporting, portfolio analysis, and business decision-making. Built and maintained data pipelines to extract, transform, validate, and consolidate financial data from multiple source systems. Applied advanced SQL performance-tuning techniques to improve query efficiency, data-processing reliability, and report response times. Developed QlikView dashboards that provided business teams with actionable insights into financial products and operational performance. Supported SAP BusinessObjects reporting by developing backend datasets, SQL logic, and data structures for enterprise reports. Translated business requirements into reusable data models, reporting datasets, dashboards, and analytical outputs. Performed data validation, reconciliation, and root-cause analysis to ensure accuracy and consistency across financial reports. Collaborated with business analysts, reporting teams, and stakeholders to deliver production-ready data and BI solutions. Built a strong foundation in financial-services data, database development, data integration, reporting, and business intelligence.

Education

Master of Technology, Artificial Intelligence, JNTU Anantapur

Bachelor of Technology - BTech, Computer Science, JNTU Anantapur