Professional Overview
I’m interested in working on AI and LLM projects, Python development, Docker environments, CI/CD pipelines, APIs, backend systems, automation tools, and scalable web applications. I enjoy solving technical challenges, improving workflows, and building reliable, efficient software solutions for businesses and long-term projects.
Experience
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Built LLM chat agents using OpenAI APIs, LangChain, and vector DBs. at scale.
- Integrated AI workflows with Python, FastAPI, and prompt pipelines. securely.
- Fine-tuned AI prompts to improve response accuracy and user retention. daily.
- Optimized token usage and latency for high-performance LLM systems.
- Built document parsing pipelines using OCR, NLP, and LLM models.
Key Achievements / KPIs
- Developed RAG systems with embeddings, semantic search, and caching. for apps
- Automated AI testing, monitoring, and hallucination detection workflows. fast
- Designed prompt engineering strategies for accurate AI task execution.
Education
Field of Study: Software Engineering
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.
Automation & AI Workflows
Build intelligent automation pipelines, integrate AI APIs into existing tools, and replace manual workflows with agentic AI systems.
MLOps & AI Infrastructure
Architect scalable ML infrastructure — CI/CD for ML, model versioning, experiment tracking, monitoring, and cloud deployment on AWS, GCP, or Azure.
AI Product & Strategy
Translate AI capabilities into product roadmaps, define success metrics, and drive adoption across engineering and business teams.
