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Agentic Data Delivery Lead
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Salary
≈ $22k – $60k per year (Estimated)
Location
In office (Pune)
Seniority
Staff · 20+ years exp
Confirmed on the employer's own hiring board on Oct 22, 2026. First seen by Alion on Aug 20, 2026. EXL scores B on the Alion truth index.
Overview
Company
Impact
Profile match
EXL, legally ExlService Holdings, is a data analytics, AI and digital operations company headquartered in New York that runs outsourced business processes and builds data and AI solutions for insurers, healthcare organisations, banks, media and retail companies. Founded in 1999 and listed on Nasdaq, it has more than 60,000 employees across six continents, with large delivery centers in Noida, Gurgaon, Pune, Bengaluru and Chennai and a newer AI hub in Dublin. It hires data scientists and data engineers, GenAI and MLOps engineers, analytics managers, solution consultants and client partners, plus talent acquisition, finance and medical coding staff.
Roles & Responsibilities
1. 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.
2. 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.
3. 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.
4. 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.
5. 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.
6. 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.
7. Stakeholder & Client Management
8. 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.
9. 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)
Roles & Responsibilities
1. 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.
2. 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.
3. 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.
4. 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.
5. 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.
6. 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.
7. Stakeholder & Client Management
8. 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.
9. 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)
Roles & Responsibilities
1. 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.
2. 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.
3. 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.
4. 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.
5. 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.
6. 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.
7. Stakeholder & Client Management
8. 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.
9. 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)
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