Deloitte Hiring 2026: Machine Learning-AI and Data Science Engineer II | Req 350771

Company :- Deloitte Consulting (US India Offices – USI)
Job Title :- Machine Learning-AI and Data Science Engineer II
Requisition Code :- 350771
Service Line / Practice :- AI & Engineering (AI & Data Offering)
Experience Level :- 3 to 6 Years
Employment Type :- Full-Time / Regular Professional
Posting Recency :- Active (September 2026)
Locations :- Bengaluru, Hyderabad, Pune, Chennai (Pan-India Multi-Location)

Deloitte’s AI & Engineering practice builds, modernizes, and operates large-scale data and AI platforms to solve complex sector-specific problems. The practice integrates cloud infrastructure, cognitive algorithms, and automated MLOps pipelines to deliver insights for global enterprise clients.

As an AI and Data Science Engineer II, you will take ownership of end-to-end Machine Learning lifecycles. Your core mandate includes leading model development, deploying algorithms to cloud environments (AWS/Azure/GCP/Databricks), optimizing data transformations using PySpark and SQL, and collaborating with cross-functional data engineering and client-side stakeholders.

Machine Learning & Data Science Engineering

• End-to-End Model Development: Design, build, evaluate, and fine-tune statistical and ML models covering classification, regression, time series forecasting, decision trees, and mathematical optimization.
• Large-Scale Data Processing: Utilize Python, PySpark, and SQL to clean, transform, and analyze structured and unstructured datasets across distributed frameworks.
• Containerization & Cloud Deployment: Package machine learning workflows using Docker, orchestrate deployments on cloud platforms (AWS, Azure, GCP, or Databricks/Snowflake), and integrate with BI dashboards (Tableau / Power BI).

Agile Execution & Leadership Support

• SDLC & Agile Delivery: Participate actively in sprint planning, retrospectives, code reviews, and defect resolution.
• Stakeholder & Client Engagement: Liaise with onsite leads and client teams to translate functional business challenges into scalable AI/ML solutions.
• Mentorship: Guide junior engineers on code quality, Git version control, and best practices in data science modeling.

Academic Qualifications

• Required Education: B.E. / B.Tech / M.C.A. / M.Sc (Computer Science or related quantitative field) from an accredited university.

Technical & Experience Requirements

• Experience: 3 to 6 years of hands-on professional experience in Data Science, Machine Learning, and Statistical Modeling.
• Core Languages & Libraries: Python, PySpark, SQL, NumPy, Pandas, Scikit-learn.
• Modeling Expertise: Classification, Regression, Time Series Analysis, Decision Trees, and Optimization techniques.
• DevOps & Platforms: Docker containerization, Git version control, Tableau or Power BI.
• Cloud & Warehouse Awareness: Familiarity with Databricks, Snowflake, or Hyperscaler ecosystems (AWS, Azure, GCP, NVIDIA platforms).

Preferred / Good-to-Have Skills

• Demonstrated ability to comprehend research papers and translate novel algorithms into practical business solutions.
• Relevant AI or Cloud certifications from premier platforms (AWS Certified Machine Learning, Azure AI Engineer, Databricks Certified ML Professional).

Estimated Compensation Expectations

For a Data Science Engineer II (3–6 years experience) at Deloitte Consulting India (USI):

• Estimated Base Package: ₹11,50,000 to ₹18,50,000 INR per annum.
• Total Annual Package (CTC): ₹13,00,000 to ₹21,00,000 INR (includes variable bonuses, corporate health benefits, continuous certification allowances, and access to DU: The Leadership Center).

1. Profile Shortlisting (Evaluation of Requisition 350771, Resume & ML/Cloud Experience)
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2. Online Technical Assessment (Python Scripting, SQL Queries, Machine Learning Algorithms & Stats)
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3. Technical Interview Round 1 (Hands-on Coding, ML Model Diagnostics, PySpark Dataframes & Feature Engineering)
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4. Technical Architecture Round 2 (End-to-End ML Pipeline Design, Containerization, Cloud Deployment & Scenario-Based Problem Solving)
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5. Managerial & Leadership Round (Agile Methodology, Client Handling & Stakeholder Management)
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6. HR Interview & Discussion (Location Selection across BLR/HYD/PNE/MAA, Shift/Travel Readiness & Offer Formalization)

• Advanced ML Concepts: Hyperparameter tuning, bias-variance tradeoff, handling imbalanced datasets (SMOTE), cross-validation strategies, and metric selection (AUC-ROC, F1-Score, MAPE).
• Distributed Computing with PySpark: Spark DataFrames vs RDDs, transformations vs actions, broadcast joins, handling data skewness, and memory management.
• Model Deployment & MLOps: Containerizing ML applications using Docker, REST API development (FastAPI/Flask) for model serving, CI/CD with Git, and cloud staging.
• Data Warehousing & Cloud: Working with Delta Lake in Databricks, querying Snowflake, and leveraging cloud-native ML services (SageMaker, Vertex AI, or Azure ML).

Technical & AI Keywords

• Machine Learning & Data Science Engineer
• Python, PySpark & Advanced SQL
• NumPy, Pandas, Scikit-Learn
• Time Series, Classification, Regression & Optimization
• Docker Containerization & Git Version Control
• Cloud ML Deployment (AWS / Azure / GCP)
• Databricks, Snowflake & Distributed Data Processing
• Tableau & Power BI Data Visualization

• End-to-End ML Lifecycle Execution
• Agile / Scrum Sprint Delivery
• Stakeholder Management & Requirements Gathering
• Peer Code Review & MLOps

1. What is the Requisition Code for this position?

The job reference code is 350771.

2. Which locations are available for this opening?

This role is available across four major Deloitte USI delivery centers: Bengaluru, Hyderabad, Pune, and Chennai.

3. What is the required experience bracket?

Candidates must possess 3 to 6 years of relevant hands-on experience in Data Science, Machine Learning, and Statistical Modeling.

4. Is cloud deployment experience strictly required?

Hands-on experience with Python, PySpark, SQL, and core ML algorithms is required. Model deployment on cloud platforms (AWS/Azure/GCP/Databricks) is listed as an added advantage that significantly strengthens candidate profiles.