Agentic Data Delivery Lead

EXL; Maharashtra; On-site; INR 4,500,000 - 7,500,000; Full time

Job summary

EXL is seeking a senior data/AI delivery leader to helm large-scale data engineering and modernisation programs in India. You will drive Agentic AI-led delivery models, govern complex pipelines, and ensure client satisfaction across multiple accounts.

You will lead CoEs, partnerships, and multi-geography teams, shaping solutions with sales and senior stakeholders. A deep background in data and AI platforms is essential.

Qualifications

• 20+ years of IT experience with solution development/engineering.
• 10+ years in data engineering and platform delivery.
• Experience in data lake/warehouse implementations and migrations.
• 3–4 years hands-on GenAI/Agentic AI delivery.
• Proven success leading large delivery teams and CoEs.
• Strong stakeholder management and global client engagement.
• Experience in RFPs, RFIs, and large deal solutioning.

Responsibilities

• Delivery Leadership & Strategy: lead end-to-end data delivery programs.
• Data Platform & Modernisation Leadership: drive migrations and lakehouse.
• GenAI Delivery: design and implement agentic AI solutions.
• Solutioning & Pre-Sales: lead RFP/RFI and proactive deal shaping.
• CoE & Capability Building: run data/AI CoEs and establish standards.
• Data Governance: enforce governance, privacy, and security controls.
• Stakeholder & Client Management: engage CXO/VP level clients.
• Partnerships & Ecosystem: grow hyperscaler partnerships and GTM.
• Leadership & People Development: mentor large cross-functional teams.

Skills

Delivery leadership

Data engineering

GenAI/Agentic AI

Stakeholder management

Global client engagement

RFP/RFI solutioning

CoE leadership

Cross-functional teams

Solution architecture

Tools

Python

Spark

Hadoop

Snowflake

Databricks

AWS

Azure

GCP

LLMs/RAG

Job description

• Lead end-to-end delivery of large-scale data engineering and modernisation programs (Data Lakes, Data Warehousing, Lakehouse, Data Migration).
• Define and drive Agentic AI-led delivery models to improve productivity across SDLC.
• Own delivery governance, quality, timelines, and client satisfaction across multiple accounts.
• Drive enterprise-level data transformations including: - On-prem → Cloud migrations
• Cloud → Cloud transformations
• Legacy DW → Modern Lakehouse / Warehouse
• Architect scalable, resilient, and future-ready data ecosystems.
• GenAI / Agentic AI Delivery
• Lead design and implementation of Agentic AI / LLM-based solutions in enterprise data ecosystems.
• Define delivery patterns for multi-agent systems, RAG pipelines, automation, and intelligent workflows.
• Drive adoption of AI-led accelerators across delivery programs.
• Solutioning & Pre-Sales
• Build value-led proposals including solution architecture, costing, and delivery models.
• Work closely with sales and account leadership in deal shaping.
• CoE & Capability Building
• Build, scale, and run Data / AI / Agentic AI Centres of Excellence (CoEs).
• Define frameworks, accelerators, reusable assets, and best practices.
• Develop internal capability maturity models and delivery standards.
• Data Governance:
• Define and enforce enterprise-wide data governance frameworks covering data quality, lineage, metadata, and access controls
• Ensure compliance with regulatory requirements, data privacy (PII), and security standards across all data and AI platforms
• Embed governance controls within data engineering pipelines and Agentic AI / GenAI delivery workflows
• Establish standards for data lifecycle management, audit readiness, and risk mitigation
• Implement AI governance practices, including model oversight, ethical AI usage, and guardrails
• Collaborate with stakeholders to drive adoption of governance policies across global delivery teams
• Engage with senior client stakeholders (CXO / VP level).
• Act as a trusted advisor on data strategy, AI adoption, and digital transformation.
• Manage multi-geography teams and global client engagements.
• Stakeholder & Client Management
• Partnerships & Ecosystem
• Drive strategic partnerships with hyperscalers and technology partners such as: - AWS, Azure, GCP
• Snowflake, Databricks
• OpenAI, Anthropic and GenAI ecosystem providers
• Influence joint GTM strategies and co-innovation initiatives.
• Leadership & People Development
• Lead and mentor large cross-functional teams (delivery, architecture, engineering).
• Build leadership pipelines and strong engineering culture.
• Drive performance, engagement, and capability development.

Roles & Responsibilities

• Delivery Leadership & Strategy
• Lead end-to-end delivery of large-scale data engineering and modernisation programs (Data Lakes, Data Warehousing, Lakehouse, Data Migration).
• Define and drive Agentic AI-led delivery models to improve productivity across SDLC.
• Own delivery governance, quality, timelines, and client satisfaction across multiple accounts.
• Data Platform & Modernisation Leadership
• Drive enterprise-level data transformations including: - On-prem → Cloud migrations
• Cloud → Cloud transformations
• Legacy DW → Modern Lakehouse / Warehouse
• Platform modernisation & digitalisation initiatives
• Architect scalable, resilient, and future-ready data ecosystems.
• GenAI / Agentic AI Delivery
• Lead design and implementation of Agentic AI / LLM-based solutions in enterprise data ecosystems.
• Define delivery patterns for multi-agent systems, RAG pipelines, automation, and intelligent workflows.
• Drive adoption of AI-led accelerators across delivery programs.
• Solutioning & Pre-Sales
• Lead RFP / RFI / proactive solutioning for large deals.
• Build value-led proposals including solution architecture, costing, and delivery models.
• Work closely with sales and account leadership in deal shaping.
• CoE & Capability Building
• Build, scale, and run Data / AI / Agentic AI Centres of Excellence (CoEs).
• Define frameworks, accelerators, reusable assets, and best practices.
• Develop internal capability maturity models and delivery standards.
• Data Governance:
• Define and enforce enterprise-wide data governance frameworks covering data quality, lineage, metadata, and access controls
• Ensure compliance with regulatory requirements, data privacy (PII), and security standards across all data and AI platforms
• Embed governance controls within data engineering pipelines and Agentic AI / GenAI delivery workflows
• Establish standards for data lifecycle management, audit readiness, and risk mitigation
• Implement AI governance practices, including model oversight, ethical AI usage, and guardrails
• Collaborate with stakeholders to drive adoption of governance policies across global delivery teams
• Engage with senior client stakeholders (CXO / VP level).
• Act as a trusted advisor on data strategy, AI adoption, and digital transformation.
• Manage multi-geography teams and global client engagements.
• Stakeholder & Client Management
• Partnerships & Ecosystem
• Drive strategic partnerships with hyperscalers and technology partners such as: - AWS, Azure, GCP
• Snowflake, Databricks
• OpenAI, Anthropic and GenAI ecosystem providers
• Influence joint GTM strategies and co-innovation initiatives.
• Leadership & People Development
• Lead and mentor large cross-functional teams (delivery, architecture, engineering).
• Build leadership pipelines and strong engineering culture.
• Drive performance, engagement, and capability development.

Must Have Skills & Experience

• 20+ years of IT experience, with strong early career foundation in solution development / engineering.
• 10+ years of experience in data engineering & platform delivery, including: - Data Lake / Data Warehouse implementation
• Data migration (On-prem to Cloud / Cloud to Cloud)
• Platform modernisation & digital transformation
• 3–4 years of hands‑on experience in GenAI / Agentic AI solutions.
• Proven experience in building and leading large delivery teams and CoEs.
• Strong experience in stakeholder management and global client engagement.
• Demonstrated experience in RFPs, RFIs, and large deal solutioning.

Technology Exposure (Mandatory)

• Programming: Python
• Data Engineering: ETL/ELT, Big Data frameworks (Spark, Hadoop ecosystem)
• Data Platforms: Snowflake, Databricks, Lakehouse architectures
• Cloud: AWS / Azure / GCP
• AI/GenAI: LLMs, RAG, Agentic frameworks, orchestration tools

Good to Have Skills

• Experience in multi-agent architectures and AI-driven automation of SDLC
• Exposure to MLOps, DataOps, and AI governance frameworks
• Experience in industry domains such as Insurance, Banking, Healthcare, Retail
• Thought leadership (whitepapers, POVs, client presentations)