MLOps Engineer

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Indias impact on the global economy has increased at an exponential rate and Deloitte presents an opportunity to unleash and realize your potential amongst cutting edge leaders, and organizations shaping the future of the region, and indeed, the world beyond.

At Deloitte, your whole self to work, every day. Combine that with our drive to propel with purpose and you have the perfect playground to collaborate, innovate, grow, and make an impact that matters.

The Team

Deloittes Technology & Transformation practice can help you uncover and unlock the value buried deep inside vast amounts of data. Our global network provides strategic guidance and implementation services to help companies manage data from disparate sources and convert it into accurate, actionable information that can support fact-driven decision-making and generate an insight-driven advantage. Our practice addresses the continuum of opportunities in business intelligence & visualization, data management, performance management and next-generation analytics and technologies, including big data, cloud, cognitive and machine learning.

Your work profile:

As an Member of our T&T Team youll build and nurture positive working relationships with teams and clients with the intention to exceed client expectations:-

Role Overview

We are looking for a skilled MLOps Engineer to design, build, and maintain scalable machine learning infrastructure. The ideal candidate will work closely with data scientists, engineers, and DevOps teams to operationalize machine learning models and ensure reliable deployment, monitoring, and lifecycle management of ML systems.

Key Responsibilities

• Develop and maintain machine learning pipelines for model training, evaluation, and deployment using Python.
• Implement and manage MLOps frameworks such as MLflow for experiment tracking, model registry, and lifecycle management.
• Deploy and monitor machine learning models in production environments.
• Build and maintain CI/CD pipelines for ML workflows using tools like Jenkins and cloud platforms such as AWS.
• Containerize ML applications using Docker and orchestrate deployments using Kubernetes.
• Manage model versioning and ensure reproducibility of ML experiments.
• Design and maintain feature stores to enable scalable feature engineering and reuse.
• Integrate ML systems with enterprise ML platforms such as Databricks.
• Implement model monitoring, performance tracking, and automated alerts for data drift, model drift, and system failures.
• Collaborate with cross-functional teams to translate business requirements into scalable ML solutions.

Required Skills

• Strong programming skills in Python.
• Hands-on experience with MLOps frameworks (MLflow).
• Experience with model deployment, monitoring, and lifecycle management.
• Experience building CI/CD pipelines using tools such as Jenkins, Git, or similar.
• Experience with cloud platforms (AWS preferred).
• Expertise with Docker and Kubernetes for containerization and orchestration.
• Understanding of model versioning, reproducibility, and experiment tracking.
• Experience managing feature stores for ML workflows.
• Experience with enterprise ML platforms like Databricks.

Preferred Qualifications

• Experience with distributed data processing frameworks such as Spark.
• Familiarity with data engineering pipelines and workflow orchestration tools (Airflow, Prefect, etc.).
• Knowledge of ML model monitoring frameworks.
• Experience working in production ML environments at scale.

Education

• Bachelors or Masters degree in Computer Science, Data Science, Engineering, or a related field.

Nice to Have

• Experience with infrastructure as code (Terraform / CloudFormation).
• Knowledge of ML security and governance practices.
• Exposure to large-scale ML systems in enterprise environments.

How youll grow

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