Lead Data En

Lead Data & AI Engineer

Role snapshot

Detail

Value

Job title

Lead Data & AI Engineer (Tech Lead)

Location

Jaipur, Rajasthan (on-site)

Experience

57 years

Employment type

Full-time

Reports to

Engineering / Technology Head

About the role

We are looking for a hands-on Lead Data & AI Engineer who can build data pipelines, ship AI-powered features and lead a small team, all at once. You will own work end to end: from SQL and ETL on Snowflake or Databricks, to RAG pipelines and AI agents, to deploying it on the cloud with CI/CD.

This role suits someone who enjoys wearing multiple hats, picks up new technology quickly and cares about delivering working software.

Key responsibilities

Data engineering

• Design, build and maintain scalable ETL/ELT pipelines on Snowflake and/or Databricks.
• Write efficient, well-tested SQL and Python for data processing, transformation and validation; model data for analytics and AI use cases.
• Ensure data quality, consistency, reliability and accuracy, and keep pipelines secure and optimised for performance and cost.
• Identify and resolve pipeline failures, data quality issues, performance bottlenecks and production incidents.

AI and GenAI

• Build Retrieval-Augmented Generation (RAG) pipelines: ingestion, chunking, embeddings, vector search, retrieval strategies and prompt design.
• Develop basic AI agents, LLM-powered applications and workflows that integrate with databases, APIs, enterprise applications and internal business data.
• Evaluate AI output for accuracy, relevance, reliability, latency and cost, and iterate on improvements.
• Apply appropriate security, access controls, data privacy and validation mechanisms when building AI solutions.
• Troubleshoot AI workflows and provide clear, actionable implementation guidance to team members.

Web development and deployment

• Build modular, reusable, testable and secure backend services and REST APIs (Python; e.g. FastAPI, Flask or Django), and lightweight front ends where needed, that expose data and AI features.
• Apply sound application architecture and web fundamentals, including authentication, error handling, logging and exception management.
• Independently debug issues across the backend, API, database and integration layers.
• Deploy and run applications on AWS, Azure or GCP, managing environments, configuration and server operations.
• Set up and maintain CI/CD pipelines and GitHub workflows with automated testing and deployment validation.
• Follow secure development practices, including secrets management, dependency vulnerability checks and access control.
• Ensure production readiness through monitoring, logging, rollback planning, post-deployment verification and incident resolution.

Engineering standards

• Follow engineering standards across all work: peer and code reviews, automated testing, documentation and production readiness reviews.

Communication & stakeholder management

• Explain technical concepts, solution designs, implementation approaches and risks to both technical and non-technical stakeholders.
• Communicate progress, dependencies, blockers, risks and delivery timelines proactively.
• Prepare clear technical documentation, architecture notes, code review feedback, deployment instructions and incident reports.

Leadership and delivery

• Lead and mentor a team of engineers through code reviews, pairing and hands-on technical guidance on SQL optimisation, pipeline design, application development, debugging, deployment and troubleshooting.
• Take end-to-end ownership of technical deliverables, from requirement clarification and estimation through implementation, testing, deployment and production stabilisation.
• Break down requirements into actionable tasks, estimate effort realistically, own delivery timelines and track progress against them.
• Identify risks, dependencies and delivery challenges early and recommend practical solutions; escalate issues with context, impact assessment and recommended next steps.
• Work closely with engineers, product teams, business stakeholders and other functions to turn needs into working solutions.
• Balance hands-on development with technical reviews, mentoring, planning and team support.
• Explore and evaluate new tools and technologies, including AI frameworks, assess their practical fit for business needs and share learnings with the team.
• Identify opportunities to improve engineering processes, automate repetitive work and increase team productivity.

Required skills and qualifications

• 5–7 years of hands-on experience in software, data or backend engineering.
• Strong SQL and Python, and solid experience with relational databases (e.g. PostgreSQL, MySQL, SQL Server).
• Proven experience building ETL pipelines, with hands-on work on Snowflake or Databricks, and sound data modelling and data quality practices.
• Web development experience, including building REST APIs (Python preferred; e.g. FastAPI, Flask or Django) with an understanding of application architecture, authentication, error handling and logging.
• Practical exposure to AI/LLMs: building RAG pipelines (ingestion, chunking, embeddings, vector search, retrieval and prompt engineering) and a basic understanding of how to build AI agents (e.g. LangChain, LlamaIndex or similar frameworks).
• Working experience with at least one cloud platform: AWS, Azure or GCP.
• Understanding of server deployment, CI/CD pipelines and Git/GitHub workflows.
• Working knowledge of secure development and production practices: secrets management, access control, automated testing, code review, monitoring and logging.
• Experience leading or mentoring engineers.
• Clear oral and written communication, with the ability to explain technical concepts to technical and non-technical stakeholders.
• Bachelor's degree in Computer Science, IT or a related field, or equivalent practical experience.

Nice to have

• Experience with vector databases (e.g. Pinecone, pgvector, FAISS, Azure AI Search).
• Familiarity with orchestration tools such as Airflow or dbt.
• Exposure to Docker and Kubernetes.
• Experience with cloud AI services (e.g. AWS Bedrock, Azure OpenAI, Vertex AI).
• Relevant cloud or data platform certifications.

Who you are

• Comfortable wearing multiple hats: data engineer, AI builder, DevOps and team lead, often in the same week.
• Genuinely curious and quick to learn new technologies, especially in the fast-moving AI space.
• Delivery-focused: you take ownership and see work through to production.
• A clear communicator who can explain technical choices to both engineers and business stakeholders.

What we offer

• A chance to build real AI and data products from the ground up.
• Ownership and leadership of a growing team.
• Exposure to modern data, cloud and GenAI technologies.
• Competitive compensation and a culture that supports continuous learning.