Engineering Manager, Data Platform & ML Ops in India at Jobgether

This position is posted by Jobgether on behalf of a partner company. We are currently looking for an Engineering Manager, Data Platform & ML Ops in India.

This role sits at the core of a fast-scaling, globally distributed, 100% remote engineering organization focused on building high-trust systems for fraud prevention and digital identity. You will lead a team responsible for the data and machine learning backbone that powers critical analytics, product intelligence, and production ML systems. Your work will directly influence how large-scale behavioral signals are transformed into reliable, production-ready models and insights used across core products. You will oversee both data platform evolution and the full ML Ops lifecycle, ensuring scalability, reliability, and performance at every layer. This is a highly cross-functional leadership role, working closely with data scientists, engineers, and product teams to turn data into measurable impact. You will also play a key role in shaping engineering culture, mentoring talent, and driving technical excellence across a rapidly evolving environment. The position is ideal for a hands-on technical leader who thrives at the intersection of data infrastructure, machine learning systems, and people leadership.

In this role, you will lead and grow a high-performing team of engineers while owning the strategy, reliability, and evolution of both the data platform and ML Ops ecosystem. You will ensure that data and machine learning systems are scalable, production-ready, and aligned with business and product needs.

• Lead, mentor, and develop a team of data platform and ML Ops engineers, fostering technical excellence and career growth.
• Own the architecture, reliability, and scalability of the internal data warehouse powering analytics and product intelligence.
• Oversee the end-to-end ML Ops lifecycle including experimentation, training pipelines, deployment, and production monitoring.
• Guide technical architecture decisions and review complex engineering proposals across data and ML systems.
• Collaborate with data scientists, product managers, and engineering leaders to translate data initiatives into business impact.
• Define and evolve engineering standards, tooling, and best practices across data and ML infrastructure.
• Drive continuous improvement in system performance, reliability, and operational maturity.
• Ensure delivery of high-impact data products that support fraud detection and identity intelligence systems.

The ideal candidate brings strong technical leadership experience across data engineering and machine learning systems, combined with a proven ability to manage teams in fast-paced, high-growth environments. You are equally comfortable guiding architecture decisions and mentoring engineers toward strong execution.

• Minimum 2+ years of experience managing engineering teams in data, ML, or platform domains.
• 5+ years of professional experience in data engineering, ML engineering, or closely related software engineering roles.
• Strong technical foundation across data infrastructure and machine learning systems in SaaS environments.
• Hands-on familiarity with ML Ops workflows including training pipelines, deployment, and monitoring.
• Experience working with modern data platforms such as ClickHouse, Databricks, Snowflake, or BigQuery.
• Proficiency with cloud infrastructure, particularly AWS-based data and ML systems.
• Proven ability to lead high-reliability engineering teams delivering production-grade data products.
• Strong communication, leadership, and cross-functional collaboration skills.

• Competitive compensation package aligned with US market benchmarks
• Fully remote, globally distributed work environment
• Opportunity to lead mission-critical data and ML systems at scale
• High-impact role influencing core product intelligence and fraud detection capabilities
• Strong engineering culture focused on autonomy, learning, and innovation
• Exposure to modern data stack and advanced ML Ops infrastructure
• Inclusive, diverse, and collaborative remote-first organization