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The job description asks for Python, SQL, software development, AI, Machine Learning, Generative AI, LLM concepts and AI/ML frameworks. Make sure your resume shows the ones you have, in the words the job uses: applicant tracking systems look for them.

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This is based in Greater Bengaluru Area.

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AI & Data Quality Engineer

Vriba Solutions

Greater Bengaluru Area

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