Professional Overview
Senior Full Stack AI Engineer with 10+ years of experience designing and delivering enterprise web applications, AI-powered solutions, and cloud-native systems. Expertise in React, Next.js, Node.js, Python, LLMs, RAG, microservices, AWS/Azure, DevOps, and scalable distributed architectures.
Experience
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Built GenAI applications using OpenAI, LangChain, and RAG pipelines
- Developed AI agents with tool calling and workflow automation
- Integrated vector databases for semantic search and retrieval
- Designed scalable microservices using Node.js and Python
- Deployed cloud-native solutions on AWS and Azure
Key Achievements / KPIs
- Reduced document search time by 85% using vector retrieval
- Improved AI response accuracy by 35% through RAG optimization
- Supported 1M+ monthly API requests with 99.9% uptime
- Cut deployment time by 70% using CI/CD automation
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Built GenAI apps using OpenAI, LangChain, and RAG pipelines
- Developed AI agents with tool calling and orchestration
- Designed semantic search using vector databases
- Built scalable Node.js and Python microservices
- Deployed AI systems on AWS and Azure cloud
Key Achievements / KPIs
- Improved AI response accuracy by 35% via RAG tuning
- Reduced retrieval latency by 85% using vector search
- Supported 1M+ monthly AI API requests
- Achieved 99.9% system uptime for production workloads
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Led AI-enabled enterprise application development
- Designed REST and GraphQL microservices architectures
- Managed AWS cloud deployments and infrastructure
- Implemented CI/CD pipelines for automation
- Delivered enterprise consulting solutions
Key Achievements / KPIs
- Delivered 12+ enterprise AI projects successfully
- Reduced release cycle time by 80%
- Improved API performance by 45%
- Scaled systems to 5x user growth
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.
MLOps & AI Infrastructure
Architect scalable ML infrastructure — CI/CD for ML, model versioning, experiment tracking, monitoring, and cloud deployment on AWS, GCP, or Azure.
Automation & AI Workflows
Build intelligent automation pipelines, integrate AI APIs into existing tools, and replace manual workflows with agentic AI systems.
