Own the production lifecycle of machine learning models, ensuring validated models are reliably deployed, monitored, optimized, and maintained at scale. The role focuses on MLOps, cloud infrastructure, automation, observability, cost optimization, and production incident management.

Responsibilities

- Own production serving, CI/CD, deployment, and monitoring of ML models.
- Build and maintain model retraining, versioning, and deployment pipelines.
- Manage ML infrastructure and optimize cloud costs through FinOps practices.
- Implement observability, alerting, model drift detection, and performance monitoring.
- Own production incident response, troubleshooting, and on-call responsibilities.
- Collaborate with Data Scientists and ML Engineers to design scalable, production-ready systems.
- Support presales and PoCs by demonstrating production readiness and scalability.
- Mentor engineers on MLOps and production-readiness best practices.
- Communicate infrastructure cost, performance, and reliability trade-offs to non-technical stakeholders.





Qualifications

- 6+ years of experience in MLOps, ML Platform Engineering, or related roles with proven production ownership.
- Robust expertise in CI/CD, containerization, cloud infrastructure, and ML observability.
- Deep understanding of the ML model lifecycle, including retraining, model versioning, and drift detection.
- Experience with Infrastructure as Code (IaC) and automated deployment pipelines.
- Strong knowledge of major cloud platforms such as AWS, Azure, or GCP.
- Experience with cloud cost monitoring, optimization, and FinOps practices.
- Strong understanding of monitoring, alerting, SLA management, and production incident response.
- Ability to troubleshoot and communicate technical incidents clearly to business stakeholders.
- Strong collaboration and mentoring skills.
- Willingness to participate in on-call and off-hours production support

📌 Forward Deployed Engineer - MLOps (India)
🏢 Systems
📍 India