AI Engineer 4A

AI Engineer Ready to turn bold ideas into real-world impact? At Genpact, we don’t just adapt to change, we lead it. AI and digital innovation are transforming the way businesses work, and we’re at the forefront of it. Genpact’s AI Gigafactory, our industry-first accelerator, exemplifies how we scale advanced technology solutions to help global enterprises work smarter, grow faster, and transform at scale. Whether tackling complex challenges through large-scale models or agentic AI, our breakthrough solutions tackle companies’ most complex challenges.

If you thrive in a fast-moving, innovation-driven environment, love building and deploying cutting-edge AI solutions, and want to push the boundaries of what’s possible, this is your moment.

Genpact (NYSE: G) is an agentic and advanced technology solutions company. We leverage process intelligence and artificial intelligence to deliver measurable outcomes. With a strong partner ecosystem and decades of client trust, we provide innovative solutions that transform how businesses run. Powered by a team with an active learning mindset and client centricity at its core, we deliver lasting value for the world’s leading enterprises. Get to know us at genpact.com and on LinkedIn, YouTube, X, and Facebook.

Job Description Role Overview We are looking for a skilled Data Engineer to join our Advanced AI Practice within the AI Application Engineering team. This hands-on individual contributor role is suited for someone who enjoys building data pipelines, supporting ETL/ELT processes, and working with cloud-based data platforms for next-gen Generative AI and Agentic AI applications. You will help deliver reliable data solutions for reporting, analytics, and AI use cases while maintaining data quality, operational stability, and delivery efficiency. Key Responsibilities Data Pipeline Development: Develop and maintain ETL/ELT pipelines to ingest, transform, and process data from multiple enterprise systems. Data Engineering: Build and support data solutions using Databricks and cloud-based platforms, including data transformations, curated datasets, and reporting data layers. Cloud and Data Platforms: Work with Databricks, Azure, AWS, and modern data platforms to support analytics and AI use cases. Data Quality and Validation: Perform data validation, reconciliation, and quality checks to ensure accuracy and consistency of data across systems. Migration and Enhancement Projects: Support data migration, modernization, and platform enhancement initiatives through testing, validation, and issue resolution. Monitoring and Support: Monitor data pipelines, investigate failures, troubleshoot issues, and support production data environments. Stakeholder Collaboration: Work with business analysts, data engineers, and application teams to understand requirements, clarify data needs, and deliver fit-for-purpose data solutions. Documentation and Best Practices: Maintain technical documentation and contribute to data engineering standards and best practices. Must-Have Skills and Experience (Mandatory) 2+ years of hands-on experience in data engineering or data platform development. Data Engineering: Experience developing and supporting data pipelines, ETL/ELT processes, data transformations, and data integration workflows. Databricks: Hands-on experience working with Databricks for data processing, transformation, and analytics workloads. Programming and Querying: Proficiency in Python and SQL for data processing, automation, and reporting requirements. Cloud Platforms: Exposure to Azure and/or AWS cloud services and data engineering solutions. Data Quality and Support: Experience performing data validation, troubleshooting data issues, and supporting production data pipelines. Deployment and Version Control: Familiarity with Git, CI/CD concepts, testing, and deployment practices. Data Fundamentals: Understanding of data modeling, database concepts, reporting, and performance optimization techniques. Good-to-Have Skills ETL and Data Integration: Exposure to ADF, Informatica, Talend, SSIS, or similar ETL tools. Reporting and Analytics: Experience with Power BI, Tableau, dashboard development, and business reporting. Data Platforms: Familiarity with Snowflake, Azure Synapse, or other cloud data platforms. Big Data Technologies: Knowledge of Spark, PySpark, Delta Lake, and large-scale data processing. Data Modeling: Understanding of dimensional modeling, star/snowflake schemas, and data warehousing concepts. Workflow and Monitoring: Exposure to Airflow, Databricks Workflows, Datadog, Azure Monitor, or similar tools. Cloud Services: Familiarity with Azure, AWS, or GCP data services. Emerging Data Technologies: Exposure to vector databases such as Pinecone, FAISS, Weaviate, or Milvus.