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).