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).
AI Engineer - Platform - CRISIL in London employer: Crisil
Crisil is an excellent employer for those looking to make a significant impact in the financial services sector. With a strong commitment to employee development, we offer comprehensive training programs and opportunities for career advancement, all within a collaborative and innovative work culture. Located in a vibrant financial hub, our team enjoys a dynamic environment that fosters creativity and professional growth, making it a rewarding place to build your career in risk management.
