Your Role
The MLOps Engineer is responsible for designing, building, and maintaining the end-to-end machine learning lifecycle, from development to production. You'll bridge the gap between data science and engineering to ensure our models are deployed reliably and at scale.
What You’ll Be Doing
- Automate and orchestrate ML pipelines for training, testing, and deployment.
- Implement CI/CD practices specific to machine learning workflows.
- Monitor and manage production ML models for performance, drift, and accuracy.
- Collaborate with data scientists to transition models from research to production-ready systems.
- Ensure scalability, reliability, and security of the ML infrastructure.
What We’d Love To See
- 3–5 years of experience in MLOps, DevOps, or a similar role.
- Strong expertise in containerization (Docker, Kubernetes) and cloud platforms (AWS, Azure, GCP).
- Proficiency in scripting languages like Python and knowledge of ML frameworks (TensorFlow, PyTorch).
- Experience with MLOps tools like MLflow, Kubeflow, or Sagemaker.
- Solid understanding of CI/CD tools such as Jenkins, GitLab, or GitHub Actions.
It’d Be Great If You Had
- Experience with monitoring tools like Prometheus and Grafana.
- Knowledge of big data technologies like Spark or Hadoop.
What You Can Expect
- Opportunity to work with a diverse and well-experienced team.
- To be part of the team who creates phenomenal growth stories for worlds renowned brands.
- Professional Growth Roadmap.
- Real-time mentorship and guidance from the leaders.
- A workplace that invests in your career, cares for you and is fun & engaging.
- You can be yourself and do amazing work.
Benefits
Interested in joining our team of artists, geeks, strategizers, and writers? If you’re a passionate, talented individual, we want to hear from you.
- Competitive salary
- Flexible work-life balance with a 5-day working Policy
- Paid time off
- Learning & Development bonus
- Health coverage
- Rewards & Recognitions
- Event & Festivals celebrations
- Ongoing training programs
- Onsite opportunities
- Recognition opportunities for open-source contributions
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