Agentic AI Delivery Lead

EXL•Jersey City, NJ

18h

About The Position

We are seeking an experienced Agentic AI Delivery Lead to drive enterprise-scale Agentic AI, Generative AI, Data Management, and Data Modernization programs. The role combines delivery leadership, solution architecture, cloud transformation, and stakeholder management to build secure, scalable, production-grade AI and data platforms on AWS. Property & Casualty (P&C) Insurance experience is mandatory.

Requirements

12+ years of experience across Data and Analytics, AI/ML, Cloud Transformation, Enterprise Architecture, or Digital Transformation.
5+ years of experience leading large-scale data modernization, data management, cloud, AI, or analytics programs.
Proven experience delivering enterprise AI, Generative AI, or Agentic AI solutions into production.
Strong delivery leadership experience across globally distributed, cross-functional teams.
Hands-on understanding of modern data architectures, data engineering, cloud-native design, APIs, security, DevOps, DataOps, and MLOps.
Strong executive communication, client management, solutioning, estimation, governance, and risk-management skills.
Property & Casualty (P&C) Insurance experience is mandatory.

Nice To Haves

Property & Casualty (P&C) Insurance experience, including exposure to policy, claims, underwriting, actuarial, billing, risk, or insurance data ecosystems.
Experience leading data migration, data management, or platform modernization programs within insurance organizations.
Familiarity with Guidewire, Duck Creek, or other insurance core platforms.
Experience with AI governance, model risk management, responsible AI, and regulated enterprise environments.
PMP, SAFe, Scrum, AWS Solutions Architect, AWS Data Engineer, AWS Machine Learning, or relevant AI/data certifications.
Business value realization and outcome measurement

Responsibilities

Lead end-to-end delivery of Agentic AI and Generative AI initiatives from discovery and solutioning through deployment, adoption, and production support.
Design and oversee autonomous and multi-agent solutions using large language models, retrieval-augmented generation, reasoning workflows, tool integration, and orchestration frameworks.
Define AI transformation roadmaps, delivery plans, success metrics, governance standards, risk controls, and reusable accelerators.
Guide the implementation of responsible AI practices covering security, privacy, explainability, human oversight, hallucination controls, and regulatory compliance.
Lead legacy-to-cloud data modernization, data migration, platform consolidation, and enterprise data transformation programs.
Define and implement data management capabilities across data governance, data quality, metadata management, master data management, data lineage, and lifecycle management.
Oversee modernization of data lakes, data warehouses, lakehouse platforms, analytics ecosystems, and real-time data processing solutions.
Ensure modern data foundations are scalable, trusted, secure, and ready to support AI, analytics, and reporting use cases.
Drive DataOps practices, reusable ingestion and transformation frameworks, and standardized ETL/ELT delivery patterns.
Provide technical oversight for cloud-native data and AI platforms using AWS services such as S3, Glue, Redshift, EMR, Lambda, Step Functions, Lake Formation, SageMaker, DynamoDB, ECS/EKS, IAM, and CloudWatch.
Drive architecture decisions across data engineering, APIs, microservices, event-driven integration, containerization, infrastructure as code, and cloud security.
Establish CI/CD, DevOps, MLOps, observability, reliability, performance, and cost-optimization practices for production workloads.
Partner with architects and engineering teams to develop scalable reference architectures and engineering standards.
Manage cross-functional global teams including AI Engineers, Data Engineers, Data Scientists, Architects, Product Owners, Business Analysts, QA, DevOps, and domain SMEs.
Own program planning, estimation, budgeting, staffing, delivery governance, dependency management, risk mitigation, and executive reporting.
Facilitate business and technology workshops to identify high-value AI and data modernization opportunities.
Communicate roadmaps, architecture decisions, delivery status, risks, and business outcomes to senior stakeholders and clients.
Mentor teams and promote engineering excellence, delivery discipline, innovation, and reusable solution patterns.
Partner with underwriting, claims, policy administration, actuarial, billing, risk, and customer-service teams to identify AI-led transformation opportunities.
Lead data modernization and AI initiatives supporting underwriting automation, claims intelligence, fraud detection, pricing analytics, risk insights, and customer servicing.
Align delivery with insurance data standards, governance expectations, privacy requirements, and regulatory controls.