Professional Overview
AI/Data Engineer with 10+ years of experience building scalable data platforms, cloud pipelines, machine learning systems, and enterprise AI solutions. Strong hands-on expertise in Python, SQL, Azure, AWS, RAG, APIs, automation, and production systems, with a focus on reliability, performance, and end-to-end delivery.
Experience
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Led AI/Data architecture for enterprise GenAI and LLM applications.
- Built a multi-tenant RAG platform on Azure.
- Developed vector and hybrid search workflows.
- Created LLM evaluation and quality checks.
- Standardized CI/CD, versioning, and observability.
Key Achievements / KPIs
- Improved grounded-answer pass rate from 78% to 91%.
- Reduced reported hallucination incidents by 40%.
- Reduced p95 latency by 31% and cost per request by 27%.
- Cut release lead time from 15 business days to 5.
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Built data pipelines for device telemetry and operational logs.
- Developed AWS-based data processing and analytics workflows
- Designed APIs for data validation and synchronization
- Built predictive diagnostics using Python and scikit-learn
- Developed OCR and computer vision workflows for Android devices
Key Achievements / KPIs
- Improved troubleshooting through automated telemetry analysis
- Resolved device-ID synchronization and data consistency issues
- Enabled low-latency, offline AI using TensorFlow Lite
- Built reusable Android/API integration patterns for enterprise apps
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Built and maintained batch data-processing jobs.
- Analyzed financial and insurance data using SQL
- Investigated production batch failures and exceptions
- Developed data validation and reconciliation workflows
- Supported data quality, troubleshooting, and production monitoring
Key Achievements / KPIs
- Improved reliability of scheduled financial processing
- Reduced recurring issues through root-cause analysis
- Built reusable workflows for exception and data-quality analysis
Areas of Expertise
AI & Machine Learning Engineers
Design, train, and ship production ML models — from data pipelines to model inference at scale. Covers supervised learning, neural networks, LLM integration, and model optimisation.
Data Science & Analytics
Extract business insights from raw data using statistical modelling, predictive analytics, A/B testing, and interactive dashboards.
