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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%

📌 AI Data Engineering (Pune)
🏢 Tata Consultancy Services
📍 Pune