AI Data Engineering

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What we are looking for
Role: AI Data Engineering
Experience Range: 8 to 10 Years
Location: Pune
Must Have:
Candidates should have hands-on experience using GitHub Copilot and engineering AI agents that combine Large Language Models (LLMs), prompt and context engineering, Retrieval-Augmented Generation (RAG), APIs, enterprise data sources, and governed tool execution to automate data, reporting, testing, and operational workflows.
Successful candidates will possess in-depth knowledge of current and emerging technologies, demonstrate a passion for designing and building elegant solutions, and continuously improve engineering efficiency through responsible, secure, observable, and testable adoption of AI technologies.
In this role, you will work with technology and business leads to build or enhance critical enterprise data applications both on-prem and in the cloud (AWS), leveraging modern data platforms such as Snowflake and Starburst.
Use GitHub Copilot as a hands-on engineering environment to analyze existing code, design and implement production-quality changes, generate and maintain tests and documentation, debug failures, review proposed changes, and verify outcomes with executable evidence.
Design, build, test, and operate AI agents that plan and execute multi-step workflows using LLM reasoning, structured prompts, context and memory, retrieval, APIs, data tools, and human approval gates to enhance data engineering, reporting, testing, and operational processes.
Develop tool-enabled agents and Model Context Protocol (MCP) integrations that securely connect AI assistants to enterprise APIs and platforms, including BI/reporting services, data products, work-management systems, and operational knowledge sources.
Implement browser and UI automation agents for authenticated enterprise workflows, evidence capture, regression testing, test generation and healing, and operational workflows, using tools such as Playwright and browser developer protocols.
Define agent evaluation, observability, and safety controls, including grounded-response checks, deterministic tool contracts, structured outputs, least-privilege credential handling, approval checkpoints, test datasets, execution traces, failure recovery, and measurable quality and productivity outcomes.
Develop and implement data mesh and data fabric architectures to enable decentralized data management and access.
Build data and BI development agents that generate and validate SQL, DAX, semantic models, reports, data-product specifications, quality tests, and deployment artifacts while preserving governance, auditability, and human review.
Develop user personas and business personas in alignment with data requirements and deliver solutions that meet business needs.
Work with business users to translate functional specifications into technical designs for implementation and deployment
Work with cross functional team members to develop prototype, produce design artifacts, develop components, perform and support SIT and UAT testing, triaging and bug fixing.
Provide problem-solving expertise and complex analysis of data to develop business intelligence integration designs
Ensure high quality and optimum performance of data systems to meet business expectations.
Good-to-Have:
Hands-on experience designing and building Agentic AI solutions using prompt and context engineering, LLMs, RAG, structured tool/function calling, APIs, memory or state management, human-in-the-loop controls, and AI governance principles.
Hands-on programming experience in Python, Java, TypeScripts and SQL, with the ability to design agent tools, API clients, MCP servers, command-line workflows, typed data contracts, and automated tests for enterprise use cases.
Experience integrating agents with enterprise authentication, APIs, databases, knowledge repositories, BI platforms, and browser automation while protecting credentials, sensitive data, and audit trails.
Demonstrated ability to evaluate agent quality through unit, integration, behavioral, and end-to-end tests; diagnose hallucinations and tool failures; instrument execution; and improve prompts, retrieval, context, and workflows using evidence.
Broader candidate background preferences -
Experience with data virtualization, data mesh, data fabric, and federated querying platform such as Denodo, Starburst or OSS platforms is highly desirable.
Experience working as a Report Visualization Engineer with Power BI, Tableau, or any similar Reporting Platforms with End-to-End delivery.
Experience with implementation of Data Modelling, Data Governance and RLS.
Experience with Enterprise Deployment Strategies and migration of legacy platform reports to modern reporting platforms.
Extract, transform, and load large volumes of structured and unstructured data from various sources into AWS data lakes or modern data platforms like Snowflake.
Solid understanding of data modeling, database design, and ETL principles.
Familiarity with data governance, data security, and compliance practices in cloud environments.
Strong problem-solving skills and the ability to optimize and fine-tune data pipelines and Spark jobs for performance.
Tableau/Power BI / Snowflake / Starburst certifications on Data related specailities are a plus.
Power Platform experience (Power Apps, Power Automate) will be a plus.
Essential:
Hands-on data and software engineering using Python, Java, TypeScripts, SQL, AWS, Snowflake, Starburst, BI platforms, GitHub Copilot, LLM/RAG and agent frameworks, MCP/API integrations, Playwright/browser automation, automated testing, observability, and secure enterprise deployment practices.
Minimum Qualification:
15 years of full-time education with minimum 50%

Work arrangement
No