End-to-End AI & Data Delivery Lead

EXL; Jersey City (NJ); On-site; USD 145,000 - 166,000; Full time

Job summary

EXL Jersey City is seeking a Delivery Lead to manage end-to-end AI and data platform projects and modern data engineering architectures. You will coordinate data engineers, ML engineers, architects, and SMEs to ensure successful delivery aligned with business goals.

You will oversee governance, deployment, monitoring, and performance tuning across the project lifecycle, while championing responsible AI and strong collaboration with stakeholders.

Qualifications

• 8+ years of experience delivering enterprise-scale data and AI projects.
• Proven success leading cross-disciplinary technical teams and implementing AI/ML solutions in production environments.
• Strong grasp of data architecture, data pipelines, prompt engineering, model deployment, and evaluation metrics.
• Excellent communication skills bridging technical and business teams.

Responsibilities

• Lead full lifecycle delivery of data and AI solutions—from discovery to adoption.
• Collaborate with solution architects, data scientists, and stakeholders to validate use cases and feasibility.
• Manage technical risks, drive issue resolution, and ensure quality and scalability.
• Oversee sprint planning, release management, and production deployment.
• Champion best practices in data governance, AI lifecycle management, model interpretability, and ethical AI usage.
• Foster strong relationships with business users, enterprise architects, and vendor partners.

Skills

8+ years exp

Cross-functional leadership

AI/ML in production

Data architecture

Communication skills

Tools

Jira

Confluence

Git

CI/CD

Agile/Scrum

Job description

EXL Jersey City is seeking a Delivery Lead to manage end-to-end AI and data platform projects and modern data engineering architectures. You will coordinate data engineers, ML engineers, architects, and SMEs to ensure successful delivery aligned with business goals.

You will oversee governance, deployment, monitoring, and performance tuning across the project lifecycle, while championing responsible AI and strong collaboration with stakeholders.