Manager, ML Operations & Data Engineering

Manager, ML Operations & Data Engineering

Job Category
:
Information Technology

Requisition Number
: MANAG
001159

• Posted :
  November 13, 2025
• Full-Time

Showing 1 location

Division: BITS

Summary

Description:

The Lead ML & Data Engineering Manager will oversee and actively contribute to the full machine learning and data engineering lifecycle — from data ingestion and feature engineering through model development, deployment, monitoring, and continuous improvement — within a cloud-native Databricks Lakehouse environment
.

This role combines hands-on technical execution with team leadership and strategic alignment
. The individual will manage and mentor a cross‑functional data team (ML engineers, data engineers, and analysts), ensuring high‑quality delivery, platform optimization, and adherence to governance and security standards.

The Lead will also make architectural and process recommendations based on industry best practices, balancing innovation with operational excellence. They will be accountable for strengthening system controls, improving efficiency through automation, and guiding the evolution of our AI and data ecosystem for scalability and sustainability.

Academic and Trade

Qualifications:

Bachelor's degree in computer science, Computer Information Systems, Computer Engineering, Math, or related technical degree from an accredited institution, and/or equivalent experience.

Work Experience:

• 5–10 years of progressive experience in data, machine learning, or software engineering roles, with a proven track record of delivering production‑grade ML and data solutions.
• At least 3 years of hands‑on experience designing, developing, optimizing, and deploying machine learning models in production environments (preferably using Databricks, Azure ML, or similar platforms).
• 2+ years of leadership experience as a technical lead, team lead, or manager overseeing data engineers, ML engineers, or data scientists — including mentoring, code review, and project delivery oversight.
• Demonstrated experience integrating ML models into operational systems, APIs, or business workflows.
• Background in data architecture, pipeline orchestration, and performance optimization across large datasets.
• Experience with in the public utility, energy, or infrastructure sector is highly desirable, particularly with applications such as load forecasting, outage prediction, grid optimization, or asset analytics.
• Proven ability to collaborate cross‑functionally with data platform, analytics, and business teams to translate organizational goals into scalable data and ML solutions.

Key Responsibilities:

• Lead, mentor, and develop a cross‑functional team of ML engineers, data engineers, and analysts.
• Translate business needs into actionable data and ML initiatives with clear milestones and measurable outcomes.
• Define and enforce team processes, standards, and best practices for data engineering, model development, and deployment.
• Manage sprint planning, prioritization, and delivery for ML and data projects.
• Collaborate closely with the Director of Data Engineering to align technical strategy with enterprise data governance, architecture, and security policies.
• Champion innovation by staying current with trends in AI, ML, and data infrastructure, identifying opportunities for continuous improvement.

Hands‑On Technical Work (50–60%):

• Design, develop, and deploy scalable, production‑ready machine learning models and data pipelines.
• Optimize workloads for cost, performance, and reliability within the Databricks Lakehouse ecosystem.
• Build and maintain feature pipelines, MLflow model registries, and CI/CD workflows for automated training and deployment.
• Process, transform, and analyze large‑scale structured and unstructured datasets.
• Integrate models into APIs, applications, or downstream systems (e.g., Azure Container Apps, Model Serving Endpoints).
• Ensure compliance with data governance, lineage, and security standards.
• Conduct code reviews, provide technical mentorship, and contribute to architecture design decisions.

Job Knowledge & Technical Expertise:

• Databricks platform experience required — including Lakehouse architecture, cluster management,…