Educational Requirements

Bachelor of Engineering, BTech, BSc, BCA, MTech, MSc, MCA

Service Line

Data Analytics Unit

Responsibilities

- Machine Learning Engineering: Design, develop, and deploy scalable ML models and AI solutions

- Build end-to-end pipelines covering data ingestion, feature engineering, model training, evaluation, and deployment

- Apply advanced techniques for model optimization, validation, and explainability

- Ensure models are production-ready with high accuracy and performance

- MLOps Lifecycle Management: Design and implement MLOps frameworks for CI/CD/CT (continuous training)

- Automate model deployment, versioning, monitoring, and rollback strategies

- Implement model performance tracking, drift detection, and alerting systems

- Use tools like MLflow for experiment tracking and model registry

- Python (OOPs) Development: Write scalable, modular, and reusable code using object-oriented Python

- Develop APIs and backend services for model serving and integration

- Implement best practices for code quality, testing, and maintainability

- Databricks Big Data: Build and optimize pipelines using Azure Databricks and PySpark

- Work with Delta Lake for data versioning and reliability

- Manage Databricks clusters, jobs, and workflows





- Optimize Spark jobs for performance, scalability, and cost efficiency

- Azure Cloud Platform: Design ML solutions using Azure services (Azure ML, ADLS, Data Factory, Key Vault, Synapse)

- Implement secure and scalable cloud architectures

- Integrate ML pipelines with Azure DevOps CI/CD pipelines

- Ensure compliance with data governance and security policies

- Data Engineering Integration: Develop robust data pipelines for ML workflows

- Handle large-scale structured and unstructured datasets

- Integrate ML models with downstream applications via APIs/microservices

Additional Responsibilities

- Preferred Skills: Experience with feature stores and model monitoring tools

- Knowledge of Docker Kubernetes (containerization)

- Familiarity with streaming (Kafka, Event Hub)

- Experience with Lakehouse architecture (Delta Lake)

- Exposure to GenAI / LLMOps (optional, added advantage)

Technical and Qualified Requirements

- Primary skills: Technology- >Data Science- >Machine Learning, Technology- >Machine Learning- >Python

Preferred Skills

- Technology- >AI-Data science- >PYTHON

- Technology- >AI-Data science- >Machine Learning

📌 MLE/MLOps, OOPs Python, Databricks, Azure Professional (Bengaluru)
🏢 Infosys
📍 Bengaluru