MLE/MLOps, OOPs Python (Deployment and Monitoring)

About Infosys

Infosys is a globally recognized consulting and IT services enterprise listed on the NYSE. The company employs over 317,000 people and has grown from a capital base of US$250 to a business valued at US$18.6 billion in LTM Q4 FY24 revenue, with a market capitalization of US$74.42 billion. Over more than four decades, Infosys has played a major role in shaping India into a leading destination for software services talent. It also introduced the Global Delivery Model and became the first Indian IT company to be listed on NASDAQ.

Role Overview

This position focuses on building, deploying, and maintaining machine learning solutions with strong emphasis on MLOps, Python development, Azure cloud, and Databricks-based workflows. The role requires hands-on work across the full ML lifecycle, from model creation to monitoring and operational support at scale.

Machine Learning Engineering

You will be expected to create, train, assess, and deploy machine learning models in production environments. The role includes designing complete ML pipelines that cover data ingestion through to model serving, while improving model quality through optimization, validation, monitoring, and lifecycle management.

MLOps and Deployment

You will help build MLOps workflows that support CI/CD/CT, along with automated deployment, version control, and model monitoring. The role also involves experiment tracking, maintaining a model registry with a preference for MLflow, and ensuring models remain reproducible, scalable, and governed properly.

Python Development

Strong object-oriented Python development is central to this job. You will write modular, reusable, and maintainable code, develop backend services and ML utilities, and ensure the codebase remains clean, testable, and well documented.

Databricks and Data Processing

The position requires working on Azure Databricks to create and improve workflows, use PySpark for data processing and feature engineering, and manage notebooks, jobs, clusters, and Delta Lake pipelines. You will also be expected to tune Spark workloads for better performance and lower cost.

Azure Cloud and Integration

Work will include using Azure services such as Azure ML, Data Factory, Blob Storage, ADLS, and Key Vault. You will deploy models and pipelines through Azure DevOps or similar CI/CD pipelines and contribute to secure, scalable, and cost-aware cloud architectures.

Data Engineering and Application Integration

The role includes building and supporting data pipelines for ML use cases, connecting models with APIs and downstream systems, and working with both structured and unstructured datasets.

Preferred Candidate Profile

The ideal candidate has 3 to 5 years of experience in Machine Learning or MLOps, strong Python OOP skills, practical exposure to Databricks and PySpark, and solid familiarity with Azure cloud environments.

Technical Requirements

Candidates should have experience with machine learning frameworks such as Scikit-learn, TensorFlow, or PyTorch, and hands-on knowledge of MLflow for tracking experiments and managing models. Familiarity with CI/CD tools like Azure DevOps, Jenkins, or GitHub Actions is also expected, along with a sound grasp of data structures, algorithms, system design fundamentals, REST APIs, and microservices.

Preferred Skills

Additional value is given to candidates who have worked with feature stores, model monitoring solutions, Docker, Kubernetes, Delta Lake, data lake and warehouse architectures, Kafka or Event Hub, and data governance or security practices.