AI Data Engineer

About the job:

Join a team where data engineering meets intelligent automation to power next-generation analytics and AI experiences. In this role, you’ll help build reliable, scalable data pipelines and enable GenAI-ready datasets that support experimentation, model development, and business decision-making. You’ll collaborate closely with analysts, data scientists, and platform teams to turn raw, fragmented data into trusted, well-modeled, and accessible assets. If you enjoy solving complex data challenges, improving performance, and bringing structure to fast-moving AI initiatives, this is a great opportunity to grow your impact. You’ll work in a culture that values ownership, continuous learning, and practical innovation-where clean data, strong engineering, and thoughtful collaboration come together to deliver real outcomes.

Responsibilities

Key Responsibilities:

• Design, build, and maintain robust ETL/ELT pipelines to ingest, transform, and curate data from multiple sources.
• Develop and optimize data models and curated datasets to support analytics, reporting, and AI/ML workloads.
• Implement data quality checks, validation rules, and monitoring to ensure accuracy, completeness, and reliability.
• Enable GenAI initiatives by preparing high-quality datasets for downstream consumption (e.g., feature-ready and retrieval-ready data).
• Collaborate with cross-functional teams to gather requirements, define data contracts, and deliver reusable data assets.
• Troubleshoot pipeline failures and performance bottlenecks; improve scalability, latency, and cost efficiency.
• Maintain documentation for pipelines, transformations, lineage, and operational runbooks to support maintainability.

Minimum Qualifications:

• BTECH, MTECH, MCA, MSC or equivalent education.
• 3-5 years of experience in data engineering with hands-on ownership of production-grade pipelines.
• Strong experience in ETL processes including extraction, transformation, orchestration, and scheduling.
• Working exposure to GenAI-oriented data preparation needs and supporting AI/ML data workflows.
• Ability to collaborate with stakeholders to translate requirements into scalable data solutions.

Technical requirements

Good to have skills:

SQL, Python, Apache Spark, Airflow, Data Modeling, data engineering, ai

Additional responsibilities

Preferred Qualifications:

• Experience designing scalable data architectures and implementing reusable data frameworks for multiple use cases.
• Familiarity with building datasets for GenAI use cases such as retrieval workflows and knowledge augmentation patterns.
• Proven ability to improve pipeline reliability through automation, alerting, and proactive monitoring.
• Strong problem-solving skills with a track record of optimizing transformations and reducing end-to-end processing time.
• Experience working in agile teams and contributing to code reviews, documentation, and engineering best practices.

Preferred skills

Technology->Data Engineering->Databricks,Technology->AI-Responsible AI->Responsible AI->explainable ai

Education

MCA,MSc,MTech,Bachelor of Engineering,BTech