The team

Deloitte’s 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

The AI/ML Engineer will be responsible for designing, developing, deploying, and maintaining machine learning and deep learning solutions across enterprise environments. This role will work closely with data scientists, data engineers, cloud architects, and business stakeholders to build scalable AI applications, production-ready APIs, and end-to-end MLOps pipelines on Microsoft Azure.

 Key skills required: 

AI/ML Development

• Design, develop, and deploy machine learning and deep learning models using PyTorch
• Work on model training, evaluation, tuning, and productionization
• Implement NLP, CV, or predictive analytics use cases (as applicable)

API Development

• Build and deploy scalable RESTful APIs using FastAPI
• Integrate ML models into production-ready APIs
• Ensure high performance, security, and scalability of backend services

Cloud & Data Engineering (Azure)

• Develop and manage AI workloads on Microsoft Azure (Azure ML, Azure Functions, AKS, etc.)
• Implement end-to-end pipelines for model deployment and monitoring
• Work with Azure Data Services (Blob Storage, Data Lake, Synapse, etc.)

Databricks & Big Data

• Build data pipelines and ML workflows using Azure Databricks
• Use Spark for large-scale data processing and feature engineering
• Collaborate with data engineers for pipeline optimization

MLOps & Deployment

• Implement MLOps pipelines using CI/CD tools
• Monitor model performance and retrain pipelines
• Ensure versioning, governance, and lifecycle management of ML models

Preferred Qualifications

• Education - Bachelor’s degree in computer science, Engineering, Data Science, Artificial Intelligence, or a related field

• 5+ years of experience in Machine Learning, Deep Learning, or AI Engineering roles

• Strong proficiency in Python and machine learning frameworks such as PyTorch

• Experience building, training, evaluating, and optimizing machine learning and deep learning models

• Hands-on experience developing RESTful APIs using FastAPI

• Experience integrating machine learning models into scalable production applications

• Strong understanding of cloud-native AI solutions on Microsoft Azure, including Azure Machine Learning, Azure Functions, Azure Kubernetes Service (AKS), and Azure Data Services
• Experience working with Azure Databricks and Apache Spark for large-scale data processing and feature engineering
• Knowledge of MLOps practices including CI/CD pipelines, model versioning, monitoring, and automated retraining workflows
• Experience with containerization and orchestration technologies such as Docker and Kubernetes

Location and Way of Working: 

• Base location: Pan India