AI & Data Quality Engineer

Position Title:

AI & Data Quality Engineer Function:

Technology Location: Bangalore

Line Manager’s Position Title: Data Engineering Manager

Key Internal Stakeholder(s): Business Teams, Data Engineering Team, AI/Data Science Team, Technology Teams

About the Organization Based in Bangalore, India, we strive to accelerate retail innovation by building competitive capabilities in Technology, Data Sciences and Business Services that enable our brands to deliver delightful experiences to our in-store and online customers.

Primary Purpose of Role

• Design and develop AI-enabled data quality solutions and tools.
• Build automated data quality, profiling, monitoring and anomaly detection capabilities.
• Develop AI/ML and Generative AI capabilities to improve data quality and engineering processes.
• Build scalable data warehouse and data engineering solutions to support AI use cases.
• Develop APIs, backend services and user interfaces for AI and data quality applications.
• Implement MLOps, CI/CD and monitoring practices for AI/ML solutions.
• Collaborate closely with business, data and technology stakeholders.

Team Member Minimum Requirements Required Qualification Level: Bachelor’s degree (Undergrad)

Qualification Field: Any engineering discipline

Preferred Qualification Level: Masters

Qualification Field: Computer Science or IT

Level of Role: Team member

Years of Experience Required for Role: 5–8 years Individual contributors provide organizational related support or service (administrative or clerical) OR roles operating in a “hands on” environment in support of daily business activities. The majority of time is spent in the delivery of support services or activities, typically under supervision.

Technical Skills Required Mandatory Skills

• Strong experience in Python, SQL and software development.
• Good understanding of AI, Machine Learning, Generative AI and LLM concepts.
• Hands-on experience with AI/ML frameworks and APIs.
• Experience building data quality, data validation, profiling or monitoring solutions.
• Strong understanding of data warehousing, data modelling and data engineering.
• Experience with cloud data platforms such as Snowflake, Databricks, Redshift or equivalent.
• Experience with AWS cloud services and cloud-based application development.
• Experience with MLOps, model deployment, monitoring and lifecycle management.
• Experience with CI/CD, Git and DevOps practices.
• Experience developing REST APIs and backend services.
• Experience with frontend technologies such as React, JavaScript or TypeScript.
• Experience with data pipelines and orchestration tools such as Airflow.
• Understanding of data governance, security and metadata management.

Good to Have Skills

• Experience with AWS Bedrock, SageMaker or equivalent AI services.
• Experience with LLM, RAG and Agentic AI solutions.
• Experience with vector databases.
• Experience with PySpark / Spark.
• Experience with Docker and Kubernetes.
• Experience with Power BI or other BI tools.
• Retail domain experience.

Traits / Abilities

• Strong problem-solving and analytical skills.
• Strong ability to translate business needs into technical solutions.
• Curiosity and willingness to learn emerging AI technologies.
• Strong ownership mindset towards solution delivery and production support.
• Ability to work across AI, Data Engineering and Technology teams.
• Strong focus on data quality, reliability and governance.
• Ability to work effectively in ambiguous and evolving environments.
• Effective stakeholder communication.

Position Scope

• AI & ML: Experience developing and integrating AI/ML and Generative AI solutions.
• Data Quality: Experience building automated data quality, validation, profiling and monitoring capabilities.
• Data Engineering & Warehousing: Experience building data pipelines, data models and scalable warehouse solutions.
• MLOps: Experience with model deployment, monitoring, versioning and CI/CD practices.
• Full Stack Development: Experience developing APIs, backend services and frontend applications.
• Cloud & DevOps: Experience with cloud platforms, Git, CI/CD and deployment practices.
• Data Governance: Understanding of data security, governance, metadata and data quality standards.
• Development Practices: Experience with Agile methodologies and SDLC.
• Communication & Collaboration: Strong stakeholder management skills with experience in gathering requirements and presenting solutions.

Area of Accountability Business Engagement

Key Responsibilities & Deliverables

• Ability to work with business stakeholders and build AI/data quality products to solve business problems.
• Liaise with business stakeholders for User Acceptance Testing.

Performance Measures & Targets

• Clear communication on estimates and minimum deviation from the planned work for the sprint.

AI & Data Quality Development Key Responsibilities & Deliverables

• Design and develop AI-enabled data quality tools, data validation, profiling and anomaly detection capabilities.

Performance Measures & Targets

• Deliver reliable and scalable AI/data quality solutions.

Development & Operations Key Responsibilities & Deliverables

• Develop APIs, data pipelines, AI/ML components and user interfaces.
• Perform code testing and production support.

Performance Measures & Targets

• Deliver bug free data products 95% of the time.
• Keep MTTR <2 hours.
• Keep availability of data/AI products at agreed SLA.

MLOps Key Responsibilities & Deliverables

• Implement CI/CD, model deployment, monitoring and lifecycle management practices.

Performance Measures & Targets

• Reliable and repeatable deployment of AI/ML solutions.

Data & Platform Key Responsibilities & Deliverables

• Develop data models and integrate with enterprise data warehouse and cloud platforms.

Performance Measures & Targets

• Maintain availability of data/AI products at agreed SLA.

Communication Key Responsibilities & Deliverables

• Keep stakeholders informed on progress of development and status of work.
• Communicate impediments, risks or challenges to business teams.
• Peer/squad feedback.

Line Manager Observations Key Challenges of This Position Developing practical, scalable and production-ready AI and data quality capabilities while integrating with existing enterprise data platforms.

Capabilities Curiosity Quickly grasps the essence of new AI, data and technology issues and concepts. Seeks opportunities to learn and apply emerging technologies.

Builds Effective Teams Contributes to positive morale and a sense of team spirit. Offers to help others complete work to ensure the team’s success.

Inspiring Communication Keeps others informed. Communicates technical concepts with an appropriate amount of detail.

Strives for Improvement Adheres to defined standards, methods and procedures. Seeks ways to work better within the processes and suggests improvement ideas.

Innovation Explores and applies emerging AI technologies to deliver practical business value.

Other Areas of Accountability Safety Accountable for a safe site for everyone, every day by implementing and evaluating relevant organisational requirements