Manager, Machine Learning Engineering, Data Science

Jobtailor; Hyderabad; On-site; INR 4,000,000 - 8,000,000; Full time

Job summary

Jobtailor is seeking an experienced leader to guide a team of ML Engineers and Data Scientists in Hyderabad. You will build, deploy, and operate large-scale ML systems, shaping production ML platforms across the full lifecycle.

You will mentor teams, drive platform capabilities, and collaborate with Product, Engineering, and Business units to translate complex problems into scalable ML/AI solutions with measurable impact.

Qualifications

• 12–15 years of total experience.
• 3+ years leading and managing ML, Data Science teams.
• Proven experience designing and delivering machine learning-driven, large-scale distributed systems in production.
• Experience building or operating end-to-end ML platforms, including pipeline orchestration, experimentation systems, model registry/promotion workflows, and production observability.
• Experience translating business problems into scalable ML/AI solutions with measurable impact.
• Hands-on experience with cloud platforms (AWS, GCP, or Azure) and modern ML/data ecosystems.
• Strong understanding of ML platforms, data pipelines, distributed systems, and production architecture patterns.
• Ability to collaborate effectively across Product, Engineering, Data, and Business teams.
• Excellent written and verbal communication skills with ability to contribute to technical design discussions and documentation.

Responsibilities

• Lead a team of Machine Learning Engineers and Data Scientists.
• Responsible for building, deploying, and operating large-scale ML systems.
• Shape production ML systems in areas such as user behavioral modeling, fraud and abuse detection, and lifecycle engagement.
• Lead the development of ML platform capabilities across the full lifecycle.
• Build and evolve core systems for ML pipelines, experimentation, model versioning/promotion, deployment, and observability.
• Stay close to the work—review designs, guide modeling approaches, and participate in key technical decisions.
• Work closely with Product, Engineering, and business teams to shape problem statements.
• Establish strong planning, tracking, and risk management practices.
• Own the bar for model performance, data quality, experimentation rigor, and system reliability in production.
• Hire, mentor, and manage a team of MLEs and Data Scientists; provide clear goals, regular feedback, and career development.

Skills

Machine Learning

Data Science

Distributed Systems

ML Pipeline Orchestration

Experimentation Systems

Model Registry

Production Observability

User Behavioral Modeling

Fraud Detection

Lifecycle Engagement

Job description

• Lead a team of Machine Learning Engineers and Data Scientists


• Responsible for building, deploying, and operating large-scale ML systems


• Shape production ML systems in areas such as user behavioral modeling, fraud and abuse detection, and lifecycle engagement


• Lead the development of ML platform capabilities across the full lifecycle


• Build and evolve core systems for ML pipelines, experimentation, model versioning/promotion, deployment, and observability


• Stay close to the work—review designs, guide modeling approaches, and participate in key technical decisions


• Work closely with Product, Engineering, and business teams to shape problem statements


• Establish strong planning, tracking, and risk management practices


• Own the bar for model performance, data quality, experimentation rigor, and system reliability in production


• Hire, mentor, and manage a team of MLEs and Data Scientists; provide clear goals, regular feedback, and career development.

Requirements

• 12–15 years of total experience


• 3+ years of leading and managing ML, Data Science teams


• Proven experience designing and delivering machine learning-driven, large-scale distributed systems in production


• Experience building or operating end-to-end ML platforms, including pipeline orchestration, experimentation systems, model registry/promotion workflows, and production observability


• Experience translating business problems into scalable ML/AI solutions with measurable impact


• Hands-on experience with cloud platforms (AWS, GCP, or Azure) and modern ML/data ecosystems


• Strong understanding of ML platforms, data pipelines, distributed systems, and production architecture patterns


• Ability to collaborate effectively across Product, Engineering, Data, and Business teams


• Excellent written and verbal communication skills with ability to contribute to technical design discussions and documentation.

Core Competencies

Demonstrates expertise in leading Machine Learning teams and managing the development of large-scale ML systems, with a strong focus on building and operating end-to-end ML platforms and ensuring model performance and data quality in production environments.

Highest-signal resume keywords

• Machine Learning Leadership


• End-to-End ML Platform Development


• Cloud Platforms (AWS, GCP, Azure)


• Data Pipeline Architecture


• Model Versioning and Promotion

ATS Optimization Keywords

Hard Skills

• Machine Learning


• Data Science


• Distributed Systems


• ML Pipeline Orchestration


• Experimentation Systems


• Model Registry


• Production Observability


• User Behavioral Modeling


• Fraud Detection


• Lifecycle Engagement

Soft Skills

• Collaboration


• Communication


• Mentoring


• Planning


• Risk Management