Engineering Production-Grade MLOps & AI Platforms
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.
Mastered Technologies
Professional Experience
A decade of engineering excellence across the UK and India.
Senior MLOps Engineer
Delivering enterprise ML infrastructure for Marks & Spencer (top UK retailer).
- Architected an automated deployment validation system that replaced manual multi-team sign-off with policy-based checks, cutting deployment lead time 83% (from 2 hours to 20 minutes).
- Managed an end-to-end ML platform on Azure, using Databricks for scalable model training and Azure Kubernetes Service for high-availability inference serving.
- Consolidated fragmented, duplicate feature tables into a centralized feature store, eliminating data silos and speeding up the feature engineering lifecycle by 40%.
- Enforced compliance and governance standards, including GDPR adherence, across the data science lifecycle for sensitive retail data.
- Designed operational dashboards to monitor model drift and system health, giving real-time visibility to both business and operations stakeholders.
- Built custom plugins for Apache Airflow and Databricks Operators to extend pipeline orchestration capability.
- Maintained a shared feature-engineering repository and coordinated requirements across multiple enterprise data science teams.
Data Scientist
Focused on Edge AI optimization and cost-effective infrastructure.
- Partnered with Intel to optimize Automatic Speech Recognition (ASR) and NLP models using the OpenVINO framework, deploying them on edge devices for Quick Service Restaurant (QSR) applications.
- Re-architected inference infrastructure for QSR clients using optimized inference engines, cutting per-store infrastructure cost by 40%.
- Built ASR models achieving a Word Error Rate of 0.1–0.2 across Banking, Retail, Food Chain, and Automotive domains.
- Worked as an end-to-end ML engineer on a food-chain domain project spanning image, speech, and text analysis.
- Built a benchmarking framework to evaluate hardware requirements for in-house AI components.
- Partnered with Nvidia on a proof-of-concept using Jetson devices for the QSR system.
Business Analyst
Led product strategy, operations analytics, and QA automation.
- Built and led a team of 70 from the ground up, generating $1.5M in Annual Recurring Revenue.
- Developed an in-house QA automation tool that cut manual data-categorization effort by 60% and raised data accuracy to 99.5%.
- Used predictive models to shape strategies addressing growth and operations issues.
- Defined and tracked KPIs for existing and new functions/projects across the company.
- Partnered with 20+ data analysts on data quality, integrity, and new approaches to presenting existing data.
- Acted as liaison across operations, technology, vendors, and clients, managing BAU support alongside project work.
- Collaborated with stakeholders to refine product requirements, functional specs, and product direction.
Content Programmer
Foundation in software engineering and web platforms.
- Developed interactive e-learning platforms for publishers and universities using Python, Django, and Flask.
Academic Background
The theoretical foundations of my engineering practices.
AI Performance Engineer Fellowship
Advanced fellowship program focusing on high-performance training, inference, and hardware-aware optimization for LLM models.
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.
Bachelor of Science (Stats, Math, CS)
Undergraduate degree in Statistics, Mathematics, and Computer Science, establishing strong computational and statistical foundations.
Professional Certifications
Validated expertise in cloud platforms and AI engineering.
Databricks — Data, ML & GenAI
Expertise in large-scale data processing and generative AI implementation on the Databricks platform.
IBM Data Science Professional Certificate
Comprehensive training in statistical analysis, predictive modeling, and data science methodologies.
GitHub Actions Certification
Advanced CI/CD automation and workflow optimization for enterprise-grade software delivery.
Featured Projects
Deep engineering dive into automated governance, LLMOps, telemetry, and edge intelligence.
🚦 Traffic Light Governance
Automated Model Governance as Code. A GitHub Actions "Gatekeeper" that enforces a quality gate on every PR — validating code quality, security posture, and model performance regressions before a single line reaches production.
🛠️ Airflow Custom Plugins
Airflow Custom Plugins for seamless integration with various services. End-to-end LLM pipeline with automated evaluation loops — GPT-4 acts as a judge to score prompt quality, enabling data-driven prompt engineering at scale with full MLflow lineage tracking.
👁️ Retail-Lens
AI-Powered Smart Shelf Vision. Computer vision system that empowers store associates to instantly identify out-of-stock items, misplaced products, and incorrect price tags — reducing shelf compliance issues in real time.
🧠 AI-Powered QSR — Edge Compliance & Benchmarking
A benchmarking study of small form factor hardware devices that manage AI workloads deployed at the edge.
⚡ Skywalker: Automated QA using Classification
A fully automated QA analysis engine using Classification & Clustering
🛠️ Serverless Model Profiler
CloudNative Hackathon submission. Developed a serverless benchmarking suite that automatically spins up isolated model inference tasks in AWS Lambda containers to profile model latency, cold-start, and memory drift.
Engineering Philosophy
Principles that guide my work in high-stakes AI production environments.
Zero-Friction for Data Scientists
Abstract away Kubernetes, Docker, and infrastructure entirely. Data Scientists should think in models and mathematics — not YAML configuration files.
Continuous Automation
From latency profiling to security scans and validation gates. If a task is performed twice, it must be codified into a reusable operator, plugin, or CI/CD runner.
Structure Begets Speed
Ad-hoc scripts don't scale to 50+ models in production. Enforcing strict project templates and CI/CD contracts makes every deployment repeatable, audit-ready, and automated.
Guardrails Enable Confidence
Strict governance (like the Traffic Light validation system) lets teams deploy faster — not slower — because safety is baked in, not bolted on.
Frugal Architecture
Cloud costs should not grow linearly with model usage. Optimised inference (ONNX, quantization, edge deployment) is not a nice-to-have; it's a first-class engineering concern.