Building the Guardrails for Enterprise AI
From Notebooks to Scalable Platforms
I specialize in building scalable, secure, and self-healing AI platforms. I help enterprises navigate the complex transition from experimental Jupyter notebooks to robust, production-grade cloud environments.
My approach focuses on creating automated governance and robust monitoring systems that empower Data Scientists rather than slowing them down. I believe that MLOps isn't just about automation—it's about building trust in AI.
Deploying LLMs and Computer Vision models to edge and cloud with zero-downtime architectures.
Mastered Technologies
Professional Experience
A decade of engineering excellence across the UK and India.
Senior MLOps Engineer
Deployed and managed end-to-end ML infrastructure for a Tier-1 UK Retailer.
- Architected a 'Traffic Light' deployment validation system, reducing deployment lead time by 83% (from 2 hours to 20 minutes).
- Managed a robust ML platform on Azure Cloud, utilizing Databricks and Azure Kubernetes Service (AKS).
- Consolidated fragmented feature tables into a centralized Feature Store, accelerating feature engineering lifecycle by 40%.
- Enforced strict compliance and GDPR standards across the Data Science lifecycle.
- Designed operational dashboards for model drift and system health monitoring.
Data Scientist (ML Engineer Focus)
Focused on Edge AI optimization and cost-effective infrastructure.
- Partnered with Intel to optimize ASR and NLP models using OpenVINO for edge device deployment.
- Reduced infrastructure cost per store by 40% through re-architected inference engines.
- Developed ASR models achieving a Word Error Rate (WER) of 0.1–0.2 across Banking and Retail domains.
Business Analyst / Product Lead
Led product strategy and automation initiatives.
- Built and trained a team of 70 professionals, generating an ARR of $1.5 Million USD.
- Developed 'Skywalker,' an in-house QA tool that reduced manual effort by 60% and improved accuracy to 99.5%.
Content Programmer
Foundation in software engineering and web platforms.
- Developed interactive e-learning platforms using Python, Django, and Flask.
Academic Background
The theoretical foundations of my engineering practices.
Post Graduate Program – AI & ML
Specialized training in Artificial Intelligence and Machine Learning, focusing on deep learning, computer vision, and NLP.
Master of Science (Mathematics)
Advanced mathematics degree providing the theoretical and statistical foundation for machine learning algorithms and data science.
Professional Certifications
Validated expertise in cloud platforms and AI engineering.
Databricks Data and GenAI Certifications
Expertise in large-scale data processing and generative AI implementation on the Databricks platform.
IBM Data Science and Data Analysis Specialization
Comprehensive training in statistical analysis, predictive modeling, and data visualization.
GitHub Actions Specialization
Advanced CI/CD automation and workflow optimization for enterprise-grade software delivery.
Featured Projects
Deep engineering dive into automated governance, LLMOps, and edge intelligence.
🚦 MLOps Traffic Light
Automated Governance System. Validates models for accuracy, latency, and security (Snyk) before production deployment.
🧞 AdGenie LLMOps
GenAI Pipeline treating 'Prompts as Code' with automated LLM-as-a-judge evaluation loops.
👁️ Retail-Lens Edge
Self-healing Computer Vision. Detects data drift on edge devices and syncs hard examples to cloud for retraining.
Engineering Philosophy
Principles that guide my work in high-stakes AI production environments.
Guardrails Enable Speed
Governance isn't a bottleneck—it builds the confidence needed to ship AI at scale without breaking systems.
Frugal Architecture
Optimized inference costs and resource allocation. Every dollar saved on cloud spend is a dollar earned for innovation.
DevEx Focus
Abstracting complexity so Data Scientists can focus on modeling while the platform handles the 'plumbing' automatically.