Hardik Goel

After leading a 14-engineer org at Paytm, chose a deep individual-contributor architect track — to design and own AI platforms hands-on rather than manage at a distance.

End-to-end architecture ownership of an AI-driven commodity forecasting platform supporting a £3.5B procurement domain, spanning MLOps governance, platform engineering, and AI-augmented engineering enablement.

Platform Architecture & MLOps Governance

• Architected and delivered a production-grade AI commodity forecasting platform supporting £3.5B in annual procurement across multiple commodities and prediction horizons.
• Designed scalable MLOps workflows on Azure ML using MLflow, SHAP, and PSI drift monitoring; established end-to-end governed deployment pipelines with DQ gates, model metadata registries, security scanning, audit logs, Microsoft Purview lineage, and Maker-Checker controls.
• Defined and enforced engineering governance standards across Data Engineering and ML — modular architecture, typed interfaces, reusable pipeline templates, and automated pytest coverage gates — improving deployment consistency and platform reliability.
• Built reusable metadata-driven data and ML pipelines integrating LSEG and enterprise RDBMS sources, reducing commodity onboarding effort and improving scalability across new forecasting use cases.
• Revamped fragmented forecasting workflows into a scalable, production-ready architecture optimised for reliability, maintainability, auditability, and developer velocity.
• Delivered stakeholder analytics dashboards and data enrichment solutions, improving commercial visibility into commodity trends and model outputs.

AI-Augmented Engineering & Agentic Framework Design

• Pioneered AI-augmented engineering workflows using Claude and GPT agents for automated code review, test generation, documentation, and pipeline scaffolding — significantly accelerating delivery velocity.
• Designed a persona-based agentic framework with specialised AI roles for Data Engineering, ML Review, Security, and MLOps Governance — one of the earliest enterprise implementations of role-based AI agents in a production data platform.
• Authored AI-enforceable markdown standards enabling automated enforcement of coding practices, security guardrails, and quality gates through structured context orchestration and RAG workflows.
• Contributed architecture consulting and platform engineering across enterprise PoCs and production adoption initiatives, accelerating AI adoption across cross-functional teams.