AI & Data Science Engineer

Genpact Ltd.; Dadri, Hyderabad, Bengaluru; Hybrid; INR 2,600,000 - 4,200,000; Full time

Job summary

Genpact Ltd. seeks an experienced Senior AI/ML Engineer to lead end-to-end modeling, training, and deployment of predictive models and GenAI solutions.

You will own the modeling lifecycle, build robust ML pipelines in Azure ML, and collaborate with data engineering, DevOps, and product teams to integrate AI into enterprise platforms.

Qualifications

• 6–8 years of hands-on experience building, validating, and deploying ML/DL models into production systems.
• Must-have modeling expertise: Proven track record delivering classification and forecasting models, especially with semi-structured data.
• Proficiency in Python and PySpark for distributed data transformation and EDA.
• Strong command of core ML/DL frameworks: Scikit-learn, PyTorch, or TensorFlow.
• Deep hands-on experience with Azure Machine Learning Workspace ecosystem (Pipelines, Model Registry, Experiments, Endpoints).
• Applied GenAI: practical experience designing LLM-based solutions including RAG architecture.

Responsibilities

• Design, train, and validate ML and DL models for DQ and forecasting use cases.
• Own core modeling and data science work, packaging models for production deployment.
• Build production-grade Python services and APIs supporting the AI lifecycle.
• Leverage Azure ML to create reproducible training pipelines and managed endpoints.
• Design GenAI capabilities, including LLMs and RAG patterns, for enterprise use cases.
• Collaborate with data engineering, DevOps, and product teams to integrate ML into platforms.

Skills

Python

PySpark

SQL

R

Tools

Azure Machine Learning

MLflow

PyTorch

TensorFlow

Scikit-learn

XGBoost/LightGBM

Databricks / Spark

Azure OpenAI

Job description

Location: Any location in INDIA

We are seeking an experienced Senior AI/ML Engineer to lead the end-to-end design, development, and deployment of predictive models, deep learning architectures, and applied Generative AI solutions.

In this role, you will take ownership of the modeling lifecycle: performing rigorous exploratory data analysis (EDA), writing distributed feature pipelines, training high-accuracy classification and forecasting models using semi-structured datasets, and packaging models into production-ready Python services. You will build and manage ML pipelines using Azure Machine Learning (Workspaces, Pipelines, and Endpoints) while collaborating cross-functionally with data engineering, DevOps, and product teams to integrate AI capabilities into broader enterprise platforms.

Relevant experience level: 6 - 8 Years

Core Stack:

• Languages: Python (Advanced/Must-have), PySpark (Must-have), SQL; R (Good-to-have)
• ML & Deep Learning: Scikit-learn, PyTorch, TensorFlow, XGBoost/LightGBM
• Cloud & MLOps Infrastructure: Azure Machine Learning (Workspaces, Experiments, Pipelines, Model Registry, Managed Endpoints), MLflow
• Generative AI: Large Language Models (LLMs), Azure OpenAI, Retrieval-Augmented Generation (RAG)
• Data Processing: Semi-structured data parsing (JSON, Parquet, Avro, XML), Databricks / Spark

Roles & Responsibilities

Design, train, and evaluate machine learning and deep learning models for a Data Quality (DQ) and forecasting use case. Own core modeling and data science work: traditional ML, deep learning, and GenAI, not CI/CD pipeline ownership or production orchestration (this sits with a separate MLOps function). Build models that are accurate, well-validated, and ready for handoff into the production environment.

Key Responsibilities

1. Machine Learning & Deep Learning Engineering

• Design, train, and validate traditional ML and deep learning models, with a dedicated focus on classification, anomaly detection, and time-series forecasting.
• Handle and process semi-structured and unstructured datasets (e.g., JSON logs, nested payloads, event streams) alongside structured tabular data.
• Architect and implement neural networks and classical algorithms using PyTorch, TensorFlow, and Scikit-learn.
• Execute structured experimentation, hyperparameter tuning, and A/B validation to optimize model accuracy, inference latency, and memory footprint.

2. Service Architecture & Azure ML Deployment

• Design, build, and maintain production-quality Python services and APIs supporting the complete AI lifecycle (data ingestion, offline training, validation, real-time/batch inference).
• Leverage Azure Machine Learning to build reproducible training pipelines, track runs in experiment registries, package artifacts, and deploy to secure, scalable managed endpoints.
• Implement responsible AI practices, monitor deployed model health, track drift (data and concept drift), and manage retraining workflows post-deployment.

3. Generative AI & Advanced Solutions

• Design and implement Generative AI capabilities, including LLM-powered applications and Retrieval-Augmented Generation (RAG) patterns.
• Develop contextual data retrieval, prompt templates, and evaluation loops for enterprise use cases.

4. Engineering Standards & Cross-Functional Collaboration

• Collaborate closely with data engineering, platform, and DevOps teams to integrate ML solutions into CI/CD pipelines and enterprise architectures.
• Conduct comprehensive code reviews, establish team best practices for model testing/linting, and mentor junior engineers.
• Maintain clear documentation for system architectures, data pipelines, model benchmarks, and experiment trade-offs for technical and non-technical stakeholders.

Minimum qualifications

• 6 - 8 years of hands-on experience building, validating, and deploying machine learning and deep learning models into production systems.
• Must-have modeling expertise: Proven track record delivering classification and forecasting models, specifically working with semi-structured data (nested JSON, log events, schema-on-read formats).
• Programming Mastery: Advanced proficiency in Python and solid hands-on experience with PySpark for distributed data transformation and EDA.
• Framework Depth: Strong command of core ML/DL frameworks: Scikit-learn, PyTorch, or TensorFlow.
• Cloud ML Platform: Deep hands-on experience with the Azure Machine Learning Workspace ecosystem (Azure ML Pipelines, Model Registry, Experiments, and Managed Online/Batch Endpoints).
• Applied GenAI: Practical experience designing LLM-based solutions, including RAG architecture and embedding-based retrieval.
• Data Handling: Strong analytical skills in SQL and handling high-volume feature pipelines.
• Communication & Delivery: Demonstrated ability to articulate technical tradeoffs, system limitations, and business impact clearly across engineering and business teams.

Preferred Skills

• Working proficiency or prior project experience with R.
• Experience with Databricks, Delta Lake, and Lakehouse architecture.
• Understanding of automated CI/CD integration with Azure DevOps/GitHub Actions for ML artifacts.

Microsoft certifications: Azure Data Scientist Associate (DP-100) or Azure AI Engineer Associate (AI-102)