Senior ML Ops Engineer

Mumbai - India

Monthly Salary: Not Disclosed

Posted on: 5 days ago

Vacancies: 1 Vacancy

Department:

Engineering

Job Summary

Description

Job Description

As a Senior MLOps Engineer you will be responsible for building and operating the tooling infrastructure and automation that enable machine learning models to be trained evaluated deployed and monitored reliably across development test and production environments. You will lead the adoption of MLOps best practices across Engineering and Data Science teams and develop strategies and platforms that allow models to be continuously delivered governed and iterated on as part of a modern cloud-native AI/ML platform.

Responsibilities

Own the ML platform infrastructure (training serving feature store model registry) with attention to cost reliability and security.

Lead initiatives to improve observability and monitoring for data pipelines and ML services including data drift model performance and latency.

Provide on-call support for production ML services; drive incident management and disaster recovery for ML workloads.

Lead the implementation and adoption of MLOps automation (CI/CD for ML model packaging deployment rollback and retraining orchestration).

Partner with Data Science and Engineering to improve reproducibility experiment tracking and model governance (versioning lineage approvals).

Establish quality gates for datasets features and models (tests validation bias/risk checks) before promotion to production.

Drive platform and tooling improvements (build in-house frameworks templates and reusable components to accelerate ML delivery).

Champion Responsible AI practices: auditability explainability access controls and compliance processes.

Implement and maintain model monitoring systems to track:

Prediction accuracy and performance metrics over time.

Data drift and concept drift detection to trigger retraining workflows.

Latency and resource utilization for inference services.

Alerts and dashboards for anomalies failures and SLA breaches.

Develop automated retraining and rollback strategies based on monitoring insights

Experience and Competencies

The following experience is expected to be successful in this role.

Required

8 years of experience in Cloud Ops

3 years of experience in MLOps ML engineering or platform/DevOps roles supporting ML in production.

Proficient with containerization and orchestration: Docker Kubernetes.

Experience building CI/CD pipelines for ML (GitHub Actions GitLab CI Jenkins).

Proficient with ML lifecycle tooling: MLflow Kubeflow TFX model registries.

Strong Python skills and familiarity with ML frameworks (TensorFlow PyTorch).

Experience deploying online/batch inference services and optimizing for latency and throughput.

Proficient with cloud platforms (AWS/GCP/Azure) and managed ML services.

Knowledge of data engineering foundations: feature stores data validation lineage.

Experience with observability: logs metrics traces (Prometheus Grafana) and model/data drift monitoring.

Solid understanding of security and governance for ML systems.

Desirable

Bachelors/Masters degree in Computer Science Engineering Data Science or related fields.

Experience with infrastructure as code (Terraform CloudFormation).

Familiarity with feature stores and data quality frameworks.

Hands-on with real-time/streaming data and online feature serving.

Experience with model explainability and Responsible AI risk checks.

Certifications in cloud ML services or Kubernetes are a plus.

Required Experience:

Senior IC

DescriptionJob DescriptionAs a Senior MLOps Engineer you will be responsible for building and operating the tooling infrastructure and automation that enable machine learning models to be trained evaluated deployed and monitored reliably across development test and production environments. You will ...