AI Engineer - Platform - CRISIL

Build, enhance, and maintain AI-powered product features with a focus on continuous product enhancement (CPE) and measurable end-user value. Engineer production-ready AI capabilities (agent tools, prompt optimisation, evaluation and observability) while collaborating closely with data science, AI engineering, and platform stakeholders.

Contribute to quality-first delivery through testing frameworks, automated checks, and model/feature validation practices. This is a platform engineering and not infrastructure. Job Duties Key Responsibilities AI Feature Development & Enhancement Build and enhance AI-driven features within existing products, prioritising incremental improvements and adoption.

Develop and maintain agent tools , connectors, and integrations to enable AI workflows. Refine and optimise prompts and interaction patterns for production use cases, including safety and reliability considerations. Support experiment tracking and model/version management (e.g., MLflow or equivalent).

Testing, Evaluation & Quality Build and maintain test functions for AI tools, including exploring agent-based testing patterns (AI validating AI) where suitable. Develop evaluation frameworks for AI/agent performance (accuracy, robustness, regressions, hallucination controls as applicable).

Implement quality gates , automated checks, and release readiness criteria for AI features. Support champion/challenger validation approaches prior to promotion. Contribute to model observability and dashboards to track quality, performance, and usage patterns.

Collaboration & Delivery Partner with Data Scientists and AI Engineers to productionise research outcomes into reliable product capabilities. Coordinate with Platform/DevOps stakeholders for deployment needs and runtime requirements (without owning infrastructure delivery).

Support tactical AI solutions in response to emerging business requirements. Participate in code reviews, documentation, and knowledge-sharing to strengthen team engineering standards. Work within agile/team delivery practices and contribute to shared sprint and release goals.

Qualification Master's degree (or equivalent practical experience) in Engineering, Computer Science, Data/AI, or a related discipline. Skills required Essential 3+ years of experience in ML/AI engineering with demonstrable experience delivering production features.

Strong proficiency in Python (designing, building, testing, and maintaining AI/ML applications). Understanding of the ML/AI lifecycle, including experimentation, evaluation, and release management. Experience with experiment tracking and model/version management concepts (tools such as MLflow are a plus).

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