Professional Overview
Senior AI/ML Engineer with 10+ years of experience building production AI systems, LLM applications, RAG pipelines, and ML platforms. Experienced with AI agents, prompt engineering, Python, PyTorch, FastAPI, cloud platforms, and MLOps. Skilled at turning complex AI concepts into reliable, scalable products that deliver real business impact.
Experience
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Built AI text-to-app platform with Python, FastAPI, and GCP for real-time apps.
- Developed LLM pipelines using Vertex AI, GPT-4o, and Claude for tool calling.
- Built intent routing models with Transformers, PyTorch, and scikit-learn.
- Created data workflows using Apache Beam, BigQuery ML, and Dataflow.
- Developed Cloud Run microservices with Firestore and PostgreSQL storage.
Key Achievements / KPIs
- Delivered production AI text-to-app platform using LLMs, FastAPI, and GCP.
- Built LLM orchestration pipelines with GPT-4o, Claude 3, and Vertex AI.
- Scaled ML data workflows using Apache Beam, BigQuery ML, and Dataflow.
- Automated MLOps lifecycle with CI/CD, Docker, and Vertex AI Pipelines.
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Built AI headshot correction system with Python, Django, and AWS APIs.
- Created synthetic data pipelines using TensorFlow, Keras, and SageMaker.
- Trained vision models with PyTorch, Albumentations, and scikit-learn.
- Automated ML data workflows using Airflow, Glue, and AWS Athena.
- Deployed MLOps stack with Docker, Terraform, MLflow, and monitoring.
Key Achievements / KPIs
- Delivered AI portrait enhancement system with scalable inference APIs.
- Built synthetic data pipelines and vision models for lighting correction.
- Automated ML workflows with Airflow, Glue, and Athena pipelines.
- Deployed MLOps platform with Docker, Terraform, MLflow, and monitoring.
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Built reinforcement learning system for refinery process optimization.
- Developed multi-agent models with adaptive decision-making capabilities.
- Created simulation environments generating synthetic RL training data.
- Optimized RL policies using PPO for stable model convergence.
- Implemented ML lifecycle tracking with MLflow, Docker, and Git.
Key Achievements / KPIs
- Built reinforcement learning system for refinery workflow optimization.
- Developed multi-agent models for adaptive industrial decision-making.
- Created simulation environments for safe RL training and validation.
- Improved ML lifecycle with reproducible tracking using MLflow and Docker.
Education
Field of Study: Computer Science
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.
NLP & Prompt Engineering
Build natural language processing systems, fine-tune large language models, and craft reliable production prompts for AI-powered products.