1 month ago

MLOps Engineer

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Source: Company careers pageValidated by: Fursa AI

Last checked: 14 hours agoStatus: Live

Job Summary

Design and maintain end-to-end ML lifecycle automation for ML models used across 2K’s global titles, from data ingestion and feature engineering to training, deployment, and continuous retraining. Own scalable model serving (API/streaming), centralized feature store consistency, and ML-focused monitoring for drift and latency. Partner with Data Scientists to productionize code, optimize GPU/CPU cluster costs, and lead technically robust, performant ML infrastructure.

Location: Bengaluru

Workplace: Onsite

Employment Type: Full time

Job Function: DevOps, Cloud & Infrastructure

Seniority: Mid level

Key Responsibilities

•Lead design and maintenance of end-to-end ML pipelines for ingestion, feature engineering, model training, and deployment.
•Architect and manage scalable model serving infrastructure (API-based or streaming) using tools like Databricks Model Serving, Seldon, or SageMaker.
•Implement and maintain a centralized feature store for consistency and low-latency real-time inference.
•Develop and optimize CI/CD for Machine Learning (Continuous Training) pipelines to automate retraining based on performance decay or new data.
•Implement ML asset monitoring and observability for model drift, feature skew, and prediction latency while optimizing GPU/CPU cluster costs.

Pay and Benefits

Perks:Health InsuranceChildcare ServicesLearning BudgetLife Assurance

Key Requirements

•Bachelor’s or Master’s degree in Computer Science, Engineering, or a related quantitative field.
•6+ years of professional experience in MLOps, DevOps, or ML Engineering focused on productionizing ML at scale.
•Deep experience with Databricks/MLflow, Kubeflow, or AWS SageMaker.
•Expert-level Python (including Scikit-Learn, PyTorch, or TensorFlow) and advanced SQL.
•Proficiency with Spark/PySpark, plus hands-on CI/CD and orchestration with Airflow (or specialized ML orchestrators).

Experience:6+ yearsMLOpsMachine learningDevOpsML engineeringGaming

Education:

Skills:Technical leadershipProblem-solvingMentorshipCross-functional collaborationStakeholder communication

Languages:English

Tech Stack:Databricks Model ServingSeldonSageMakerDatabricksMLflowKubeflowPythonScikit-LearnPyTorchTensorFlowSQLSparkPySparkFeature storeAPIStreamingContinuous Training (CT)Model retrainingAirflowDocker

Company Brief

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