MLOps at Qloron in Mumbai, Maharashtra, IN

We are looking for an MLOps Engineer to own the operational infrastructure of the platform. You will be responsible for deploying models reliably into production, building the infrastructure that keeps them running at scale, and ensuring that model performance is continuously tracked and maintained. You will set the MLOps standards for the squad and own the full lifecycle from model handoff to production monitoring and retraining.

Key Responsibilities

ML Infrastructure & Tooling

• Set up and own the ML infrastructure on the data platform — model registry, experiment tracking, feature store, and serving infrastructure.

• Define and enforce MLOps standards — how models are versioned, how experiments are tracked, and what production readiness criteria must be met before deployment.

• Build and maintain the CI/CD pipelines for model development and deployment, enabling the team to ship model updates reliably and repeatedly.

Model Deployment & Serving

• Own the deployment of all models to production — from the ML Engineer’s trained model to a stable, monitored, serving endpoint.

• Design and implement model serving infrastructure capable of handling real-time inference requirements with appropriate latency and throughput.

• Build fallback mechanisms for all deployed models — when model confidence falls below defined thresholds, the system must degrade gracefully to rule-based logic rather than produce unreliable outputs.

• Manage model versioning and rollback capabilities so that problematic deployments can be reversed quickly and safely.

Monitoring & Drift Detection

• Build and maintain model performance monitoring dashboards covering accuracy, latency, throughput, and data drift indicators.

• Define performance thresholds for each deployed model and implement alerting when models deviate from expected behaviour.

• Build automated retraining pipelines triggered by drift detection or performance degradation, ensuring models stay current as data and regulations evolve.

• Maintain a feedback loop between validator decisions in the application layer and model retraining workflows.

Production Reliability

• Own the operational readiness of the AI system for production deployment — monitoring, alerting, runbooks, and incident response procedures.

• Work with the Backend Engineer to ensure model inference endpoints meet the latency and reliability requirements of the integrations that depend on them.

• Conduct regular load testing and capacity planning to ensure the serving infrastructure scales with growing transaction volumes.

Required Qualifications

Education

• B.E. / B.Tech / M.Tech in Computer Science, Information Technology, or a related field.

Experience

• 5+ years of engineering experience with at least 3 years focused on MLOps or ML infrastructure in a production environment.

• Proven experience deploying and maintaining ML models at production scale — not research or experimentation contexts.

• Experience building model monitoring and automated retraining pipelines.

Technical Skills

• ML Platforms: MLflow for experiment tracking and model registry; Databricks MLOps stack strongly preferred.

• Model Serving: Experience with model serving frameworks — TorchServe, TensorFlow Serving, BentoML, or equivalent; Databricks Model Serving a plus.

• Infrastructure: Docker and Kubernetes for containerised model deployment; Terraform or equivalent for infrastructure-as-code.

• CI/CD: GitHub Actions, GitLab CI, or equivalent for automated model deployment pipelines.

• Monitoring: Prometheus, Grafana, or equivalent for infrastructure and model performance monitoring; experience with data drift detection frameworks.

• Languages: Python proficiency; familiarity with Bash scripting for automation.

• Cloud: Experience with at least one major cloud provider — AWS, Azure, or GCP — for managed ML infrastructure services.

Preferred Qualifications

• Experience operating ML systems with real-time inference requirements in an enterprise setting.

• Familiarity with feature stores and their role in maintaining consistency between training and serving environments.

• Prior work in fintech, compliance, or similarly regulated environments where model explainability and auditability are requirements.

• Experience with A/B testing and shadow deployment patterns for safe model rollouts.

• Contributions to MLOps open-source tooling or community knowledge sharing.