Staff AI Engineer

Imagine Learning; Bengaluru; On-site; INR 900,000 - 1,500,000; Full time

Job summary

Imagine Learning seeks a Staff AI Engineer, a senior individual contributor responsible for the technical direction and engineering capability for Artificial Intelligence systems across Imagine Learning. This role sits in the AI and Data leadership group, collaborating with peers to maintain high standards, engineering practices, and delivery quality in AI capabilities.

You will partner with Product Management, Data Science, and Data Platform teams to define and execute AI engineering strategy,

Qualifications

• Master's degree in CS/ML/Data Science or related field with 10+ years in software engineering.
• Experience shipping production AI/ML systems in enterprise-grade environments.
• Strong architecture and design for modern AI systems including LLMs, RAG, and agentic workflows.
• Hands-on AI evaluation: benchmarks, human-in-the-loop, automated grading, and testing.
• Production service development, testing, and lifecycle management.
• Experience with AWS or other cloud providers for AI workloads.
• Knowledge of data platforms (e.g., Snowflake) and data modeling impact on AI quality.
• Understanding of Enterprise Architecture and full SDLC including AI considerations.
• Ability to navigate compliance, privacy, and safety for AI serving minors.
• Proven track record as a senior IC influencing multiple teams across geographies.
• Excellent communication, presentation, ownership, and problem-decomposition skills.
• Mentoring engineers across multiple teams.

Responsibilities

• AI engineering and architecture direction for one or more teams.
• Develop AI Engineering and Architecture Strategy and Roadmap with PM, DS, and Data Platform.
• Participate in requirements, solution architecture, prototyping, productisation, and shipping quality code.
• Prototype novel AI approaches in collaboration with peers.
• Own AI evaluation capability: frameworks, tooling, and practices.
• Own synthetic data generation for benchmarking, safety testing, and capability development.
• Set standards for AI design, evaluation, code quality, and tooling.
• Collaborate with Product, Platform, Data, and Security teams.
• Partner with engineering managers to drive AI delivery maturity and production readiness.
• Ensure metrics show progression in AI capability and product quality.
• Work with Production Engineering/Operations for platform availability and performance.
• Define CI/CD requirements for AI workflows (model versioning, gates, deployment).
• Increase velocity of quality AI feature delivery via automation and reusable infra.
• Improve reliability and measurability of AI systems year over year.
• Represent AI strategy to engineering org and at forums.
• Other duties as required.

Skills

AI engineering

Architecture design

Cloud platforms

Agile development

Communication skills

Mentoring/coaching

Problem solving

Education

Master's degree in Computer Science or related field

Tools

Snowflake

SQL

NoSQL

Vector data stores

AWS/cloud services

Job description

The Staff AI Engineer is a senior individual contributor responsible for the technical direction and engineering capability for Artificial Intelligence systems across Imagine Learning. This position is part of the AI and Data technical leadership group, collaborating with a peer group of senior individual contributors to set and maintain a high bar with respect to standards, engineering practices, and delivery quality in the AI capability area. This role ensures measurable improvement in the quality and reliability of AI systems shipped to customers, robust management of AI-specific technical risk, and productive collaboration with engineering teams across the organisation to define and execute AI engineering strategy.

Responsibilities:

• Responsible for AI engineering and architecture direction for one or more teams.
• Create and execute, in partnership with Product Management, Data Science, and Data Platform functions, the AI Engineering and Architecture Strategy and Roadmap for their responsible area.
• Participate in all aspects of AI engineering, including product requirements, solution architecture, prototyping, productisation, and shipping quality code.
• Identify, develop, and execute novel AI engineering approaches and applications, independently and in partnership with product, engineering, and data science peers, contributing to innovation in the organisation's AI capability.
• Own the capability area for AI evaluation, including the frameworks, tooling, and practices required for the organisation to measure and improve the quality of its AI systems.
• Own the capability area for synthetic data generation in support of benchmarking, safety testing, and capability development.
• Work with the peer group to establish the relevant standards for AI solution design, evaluation, code quality, and developer tooling for AI workflows.
• Collaborate with teams to support inflight strategies and other roadmaps as required, including Product, Platform, Data, and Security.
• Partner with engineering managers to drive higher engineering maturity in AI delivery and quality, including review of technical designs and production readiness.
• Ensure key metrics are met that highlight progression in AI capability and quality of shipped products.
• Partner with Production Engineering and Operations to ensure platform availability and performance, as related to AI services.
• Partner with Platform Engineering and DevOps to articulate CI/CD requirements specific to AI workflows, including model versioning, evaluation gates, and deployment processes.
• Drive increased velocity of quality AI feature delivery through simplification, automation, and reusable infrastructure.
• Significantly improve the reliability and measurability of AI systems year over year across the portfolio.
• Represent technical AI strategy to the engineering organisation, including presentation at engineering forums and partnership with engineering leadership.
• Other duties as required.

Requirements:

• Master's degree in Computer Science, Machine Learning, Data Science, or a related field, and 10+ years of technical experience in software engineering, including substantial hands-on experience shipping Artificial Intelligence or Machine Learning systems to production; or an acceptable combination of education and experience.
• Demonstrated experience designing, building, and operating production AI applications at enterprise-grade, including evaluation, observability, safety, and version management.
• Strong architecture and design experience with modern AI systems, including Large Language Model applications, Retrieval Augmented Generation architectures, agentic workflows, and related patterns.
• Hands-on expertise with modern AI evaluation practice, including benchmark design, human-in-the-loop evaluation, automated grading, adversarial testing, and operationalisation of evaluation as an ongoing engineering function.
• Strong engineering fundamentals in one or more relevant programming languages, including production service development, testing, and lifecycle management.
• Strong understanding and experience with Amazon Web Services or other cloud providers, including services relevant to AI workloads.
• Working knowledge of modern data platforms (such as Snowflake) and understanding of how data modelling decisions affect AI application quality.
• Strong grasp of data management and different database technologies, including SQL, NoSQL, and vector data stores.
• Understanding of Enterprise Architecture and experience with building and executing technical strategies.
• Full understanding of agile development practices and managing a full software development lifecycle, including considerations specific to AI and Machine Learning systems.
• Demonstrated ability to navigate compliance, privacy, and safety considerations at an enterprise level, including awareness of considerations specific to AI systems serving minors.
• Demonstrated track record of innovation in AI engineering, including the ability to identify and prototype novel applications and approaches, and to take them from concept to production.
• Proven track record as a senior individual contributor influencing engineering practice across multiple teams and geographies without direct line management authority.
• Must have strong communication skills, presentation skills, ownership acumen, and be able to deconstruct complex problems and projects into execution detail.
• A passion for learning and teaching others, including mentoring and coaching engineers across multiple teams.
• Strong organisational skills and structured in execution.