About the role

Entrupy is seeking a ML-Ops Engineer to join our growing team supporting machine learning infrastructure and operations. This is a great opportunity for someone early in their ML-Ops or data engineering career who is excited to work on real-world AI products and wants to grow their experience in deploying and managing machine learning systems in production.

As part of this role, you'll collaborate with experienced engineers, data scientists, and product teams to help streamline ML workflows—from model training and evaluation to deployment and monitoring.

Location: Bangalore, India (Hybrid)
Reports To: VP of Engineering

This role involves a mix of technical contribution, and operations work. In addition, this role will serve as a point of contact with various US-based and IN-based teams responsible for model delivery: annotation teams, infrastructure engineers, machine learning engineers, and products.

Some project areas this team is responsible for include:

• Infrastructure and libraries to define, deploy, run, and monitor training and inference jobs
• Providing interfaces and tooling for ML engineers to work with
• Job graph visualization and analytics
• Bringing research models and code to production
• Hybrid cloud server provisioning and automation
• Internal dashboards and annotation tools
• Contributing to best practices and methodology guidelines for data science teams
• Platform advocacy, training, and mentoring
  
  

What You'll Do:

• Collaborate with engineering leads to define and implement new features and subsystems for our machine learning platform.
• Build interfaces and tools for ML researchers, engineers, and data teams.
• Operationalize research models — bringing them from prototype to production and scaling them effectively.
• Design, build, and maintain data and training pipelines, job orchestration systems, and monitoring setups.
• Develop and maintain automated testing and contribute to integration testing and rollouts.
• Assist research and product teams in the use of the platform.
• Stay in regular communication with ML research and product teams to understand how the platform can best assist in other teams' objectives.
• Collaborate with infrastructure teams for provisioning, deployment workflows, and automation.

Who you are:

• 3-5 years of experience working in ML-Ops, data engineering, backend development, or DevOps.
• Exposure to deploying or maintaining ML models in real-world applications (internships or full-time roles).
  Experience writing Python scripts and familiarity with software engineering best practices (e.g., version control, testing).
• Comfortable working with cloud platforms (AWS preferred) or containerized environments like Docker.
• Curious, proactive, and eager to learn from more experienced team members.

Good to have

• Experience with job schedulers or orchestration tools (e.g., Airflow).
• Familiarity with model monitoring tools (e.g., Prometheus, Grafana).
• Exposure to experiment tracking tools like MLflow or Weights & Biases.
• Interest or experience in working with automation and infrastructure-as-code tools (e.g., Terraform).
• Prior experience in startup or cross-functional team environments.
• Familiarity with Anyscale Ray for scalable machine learning workloads is a plus.

What we offer

• Market competitive and pay equity-focused compensation structure
• Generous time away including company holidays, paid time off, sick time, parental leave, and more!
• Rich medical benefits and insurance coverage
• Opportunity to be part of the diverse and growing team across Japan, US, India, and Brazil.
  
  

  We have had an incredible run so far and laying the foundation for a culture that is fast-paced, entrepreneurial, and rooted in passion, kindness, and positivity. We live by these values – we hire by them, promote them, and celebrate them every day.

  Please apply if you want to be a part of a collaborative and dynamic team with a passion for working with high-end luxury brands and cutting-edge technology and want to be at the forefront of AI innovation.