The National Office, a key Strategic Enabler in our new model, is comprised of professionals with various areas of focus that collectively provide mission critical services to support Deloitte’s overall success. Whether focused on project financial, quality and risk management, methods and tools, sales excellence, talent, leadership support, or other activities these professionals ensure that our Firm operates efficiently, and our people are able to effectively serve clients every day.

Position Summary

Level: Consultant / Senior Consultant or equivalent

As a Consultant / Senior Consultant at Deloitte Consulting, you will design, build, and scale production-grade machine learning systems and platforms. You will work closely with cross-functional teams to translate business and product requirements into scalable ML solutions, while ensuring robust engineering practices, reliability, and performance across the ML lifecycle.

Work you’ll do

·       Partner with Product Managers, Data Scientists, and Engineering teams to productionize machine learning models and integrate them into scalable product platforms.

·       Own the design and evolution of production ML systems, including data pipelines, model training workflows, deployment infrastructure, and monitoring frameworks.

·       Design, build, and maintain end-to-end ML pipelines covering data ingestion, feature engineering, model training, validation, and deployment.

·       Develop and maintain production-grade ML services and APIs for reliable model serving at scale.

·       Implement and maintain MLOps practices, including automated training pipelines, model versioning, CI/CD workflows, and reproducible experimentation.

·       Deploy and manage ML systems in cloud environments ensuring high availability, reliability, and performance.

·       Implement model monitoring and observability frameworks to track model performance, drift, data quality issues, and system health.

·       Work closely with Data Scientists to convert experimental models into efficient, scalable, and production-ready implementations.

·       Build scalable data processing pipelines using distributed computing frameworks to support training and inference on large datasets.

·       Contribute to the architecture and evolution of the ML platform, helping define scalable patterns for model development, deployment, and lifecycle management.

·       Ensure production ML systems meet performance, reliability, and scalability requirements, including latency and throughput constraints.

·       Provide technical mentorship and code reviews to support strong software engineering and ML system design practices across the team.

·       Collaborate across distributed teams to deliver AI-driven products that solve complex client challenges.

·       Provide feedback to product teams to continuously improve platform capabilities and developer experience.

·       Ensure software development lifecycle (SDLC) standards, testing practices, and engineering quality guidelines are consistently followed.

·       Continuously explore and apply emerging technologies across AI/ML, distributed systems, and cloud platforms.

The team

ConvergeCONSUMER™ is a product-driven business that combines differentiated consumer insights with next-generation decision and experience platforms to help consumer-focused businesses optimize decision making and deliver personalized experiences to drive growth, consumer loyalty and profitability. We operate with the speed and agility of a startup, inside the world’s largest professional services firm.

Qualifications

Must Have Skills/Project Experience/Certifications

·       Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related technical field.

·       5+ years of experience building and deploying machine learning systems in production environments.

·       Strong software engineering experience with Python and building scalable applications.

·       Experience designing and implementing production ML pipelines and model lifecycle workflows.

·       Experience working with distributed data processing frameworks such as PySpark or Spark.

·       Hands-on experience implementing MLOps practices, including automated model training, CI/CD pipelines, model versioning, and deployment automation.

·       Experience deploying ML systems on cloud platforms such as AWS, GCP, or Azure.

·       Familiarity with containerization and orchestration technologies such as Docker and Kubernetes.

·       Experience working with large-scale datasets and distributed data processing infrastructure.

·       Experience using ML frameworks such as Scikit-learn, TensorFlow, PyTorch, or similar libraries.

·       Strong understanding of feature engineering pipelines, model lifecycle management, and model monitoring strategies.

·       Strong collaboration and communication skills with the ability to work effectively with cross-functional teams including product managers, data scientists, and engineers.

·       Demonstrated ability to take ownership of complex technical systems and deliver production-ready solutions.

·       Ability to work effectively across distributed teams and time zones.

Good to Have Skills/Project Experience/Certifications

·       Experience building and operating real-time or near real-time ML inference systems.

·       Experience designing or maintaining feature stores or shared ML data infrastructure.

·       Familiarity with time-series forecasting systems or demand forecasting use cases.

·       Experience implementing model monitoring, drift detection, and automated retraining strategies.

·       Experience working with consumer datasets or marketing data in industries such as retail, consumer products, automotive, transportation, or hospitality.

·       Experience contributing to internal ML platforms, developer tooling, or reusable ML infrastructure.

Education

BE/B.Tech/M.C.A./M.Sc (CS) degree or equivalent from accredited university

Location

Bengaluru/Hyderabad/Pune/Chennai

Shift Timings

11 AM to 8 PM or as per business requirements