Build, enhance, and maintainAI-powered product featureswith a focus oncontinuous 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 throughtesting frameworks, automated checks, and model/feature validationpractices. This is a platform engineering and not infrastructure.
Key Responsibilities
- Build and enhance AI-driven features within existing products, prioritising incremental improvements and adoption.
- Develop and maintainagent tools, connectors, and integrations to enable AI workflows.
- Refine and optimisepromptsand interaction patterns for production use cases, including safety and reliability considerations.
- Supportexperiment tracking and model/version management(e.g., MLflow or equivalent).
Testing, Evaluation & Quality
- Build and maintain test functions for AI tools, including exploringagent-based testingpatterns (AI validating AI) where suitable.
- Develop evaluation frameworks for AI/agent performance (accuracy, robustness, regressions, hallucination controls as applicable).
- Implementquality gates, automated checks, and release readiness criteria for AI features.
- Supportchampion/challengervalidation approaches prior to promotion.
- Contribute tomodel observabilityand dashboards to track quality, performance, and usage patterns.
- 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.
Skills required:
- 3+ yearsof experience in ML/AI engineering with demonstrable experience delivering production features.
- Strong proficiency inPython(designing, building, testing, and maintaining AI/ML applications).
- Understanding of theML/AI lifecycle, including experimentation, evaluation, and release management.
- Experience withexperiment trackingand model/version management concepts (tools such as MLflow are a plus).
- Strong communication skills with the ability to work effectively across technical and business stakeholders.
Preferred
- Hands-on experience withMLOps tooling(e.g., MLflow).
- Exposure toAI/agent frameworks(e.g., LangChain, LangSmith, CrewAI, Azure AI Agent Service, AWS Strands or similar).
- Experience withGenAI application development, including prompt iteration and agent/tool patterns.
- Familiarity with cloud platforms (AWS/Azure) and/or data platforms (e.g.,Databricks).
- Working knowledge ofGitandCI/CDworkflows.
- Experience infinancial servicesor other regulated environments.
Supporting Tools (Working Knowledge)
- Python ecosystem for AI/ML development (testing, packaging, experimentation)
- Git-based development workflows and code review practices
Observability/monitoring dashboards for AI feature performance (tooling varies by environment)
#J-18808-LjbffrAI Engineer - Platform employer: Crisil
As a leading investment banking firm, we pride ourselves on fostering a dynamic work environment that encourages innovation and collaboration. Our commitment to employee growth is evident through comprehensive training programs and opportunities to engage in large-scale technology transformations, particularly in the Market Risk and Counterparty Credit Risk domains. Located in a vibrant financial hub, we offer competitive benefits and a culture that values diversity and inclusion, making us an exceptional employer for those seeking meaningful and rewarding careers.
