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Lead enterprise data architecture, modernization, and transformation initiatives across insurance and BFSI environments. Design data lakes, warehouses, and lakehouse platforms; establish data governance, security, compliance, and multi-cloud strategies. Oversee program delivery, Agile/SAFe execution, release management, environment strategy, DevOps, performance optimization, and engineering excellence. Enable AI/ML and advanced analytics through scalable data platforms while leading enterprise architecture and delivery teams.

Develop and support real-time data processing solutions using Confluent Kafka and Apache Flink. Configure Kafka brokers, Connect, Schema Registry, and source/sink connectors; process nested JSON and Avro data; and work with AWS services including S3, Lambda, and RDS. Responsibilities include troubleshooting, incident and problem management, root-cause analysis, documenting best practices, and providing technical guidance for highly available streaming environments.

Designs, develops, and configures SAP MDG solutions, including change requests, process and data models, workflows, integrations, and data replication. Supports L2/L3 production issues, UAT, go-live, prototypes, S/4HANA migrations, and implementation projects covering master data domains such as materials, customers, vendors, and finance. Translates business requirements into technical specifications and uses ABAP, web services, BRF+, BADI, FPM, and migration tools.

Design, develop, and support enterprise integration solutions using MuleSoft Mule 4.x and Anypoint Platform. Build System, Process, and Experience APIs; create REST and SOAP integrations; transform data with DataWeave; manage APIs, deployments, monitoring, security, and error handling; write MUnit tests; troubleshoot production issues; support Git-based CI/CD; and produce technical documentation while collaborating with architects, QA, business teams, and developers.

Perform functional, integration, data migration, regression, E2E, UAT, and cutover testing for Microsoft Dynamics 365 F&O and SCM. Test integrations with PLM and financial reporting systems, validate financial data and reconciliations, maintain regression suites, and manage defects through Azure DevOps. Support post-go-live ownership, parallel-run reconciliation, AI feature testing, and enterprise ERP quality assurance.

Design and lead enterprise agentic AI architectures on AWS, including multi-agent workflows, RAG solutions, LLM integrations, and API-based tools. Build production systems with Bedrock, AgentCore, SageMaker, LangGraph, CrewAI, and LlamaIndex. Establish security controls, identity management, guardrails, observability, tracing, evaluation pipelines, and managed-service deployments. Collaborate with enterprise teams and communicate architecture decisions effectively.

Build AWS-based knowledge graph, semantic layer, ontology, and AI-enabled data solutions. Develop graph and vector search, LLM grounding, RAG, semantic pipelines, and scalable ETL workflows using services such as Neptune, S3, Glue, Lambda, Step Functions, OpenSearch, SageMaker, and Bedrock. Collaborate with architects, engineers, clients, and delivery teams while supporting proofs of concept, demonstrations, reusable assets, and solutioning. The role also involves integrating generative AI capabilities with enterprise systems and leading knowledge engineering initiatives.

Architect enterprise Salesforce Revenue Cloud Advanced, CPQ, Billing, and Quote-to-Cash solutions. Design product catalogs, pricing, subscriptions, billing, order management, integrations, and financial workflows. Lead ERP, payment, tax-engine, and event-driven integrations using REST/SOAP, MuleSoft, or Boomi. Guide Salesforce development teams through solution design, documentation, testing, deployment, and go-live, while supporting AI, GenAI, and Agentforce use cases.

Conduct business analysis and research, gather and synthesize requirements, evaluate evidence, analyze data using Excel or SQL, and produce clear reports, memos, documentation, and actionable insights. The role requires strong analytical judgment, professional writing, stakeholder communication, critical thinking, and the ability to explain complex business findings. Work supports the training and improvement of next-generation AI systems, though prior AI/ML experience is not required.

Designs, builds, integrates, tests, and operationalizes enterprise-grade AI and LLM infrastructure. Responsibilities include architecting scalable Generative AI and agentic AI solutions, optimizing performance and cost, evaluating platforms and vendors, deploying LLM applications, and ensuring reliability, security, scalability, and operational readiness. The role collaborates with engineering and architecture teams to deliver production-ready AI platforms.