Professional Overview
Senior AI/ML Engineer with 10+ years of experience building production AI systems using LLMs, RAG, NLP, Python, PyTorch, FastAPI, and cloud platforms. Experienced in designing scalable ML pipelines, deploying intelligent applications, and turning AI prototypes into reliable real-world solutions.
Experience
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Built AI concierge platform with LLMs, RAG, NLP, and scalable backend services.
- Developed multilingual NLP pipelines using Python, PyTorch, Hugging Face, and sp
- Integrated OpenAI LLMs with guardrails, retrieval, and deterministic workflows.
- Built FastAPI microservices with Kafka, Redis, gRPC, and async processing.
- Deployed ML systems on AWS with Docker, Kubernetes, Terraform, and monitoring.
Key Achievements / KPIs
- Built production LLM systems with RAG, NLP, and AI automation workflows.
- Designed scalable ML pipelines for real-time multilingual processing.
- Deployed cloud AI services using AWS, Docker, Kubernetes, and Terraform.
- Improved reliability with monitoring, guardrails, and fallback systems.
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Built NLP system to extract booking data from customer emails automatically.
- Developed ML pipelines with spaCy, Transformers, and Scikit-learn.
- Created FastAPI services with Celery, RabbitMQ, and Redis workflows.
- Built data pipelines using Pandas and Airflow for ML datasets.
- Deployed Azure ML workloads with AKS and scalable cloud services.
Key Achievements / KPIs
- Automated booking email processing with NLP and ML models.
- Built scalable Azure ML pipelines for production workloads.
- Developed reliable APIs and async processing workflows.
- Delivered dashboards for operational insights and tracking.
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Built AI grading platform using ML models for automated assessment workflows.
- Developed evaluation pipelines with PyTorch, Transformers, and RAG approaches.
- Designed async processing systems with Kafka, Redis, and PostgreSQL.
- Deployed scalable services on GCP using GKE, Cloud Run, and Docker.
- Integrated LMS platforms with REST APIs and OAuth authentication.
Key Achievements / KPIs
- Built AI grading workflows using ML, Transformers, and RAG methods.
- Automated assessment processes with scalable backend pipelines.
- Deployed cloud-native ML services on GCP infrastructure.
- Integrated LMS platforms with secure APIs and authentication.
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.
MLOps & AI Infrastructure
Architect scalable ML infrastructure — CI/CD for ML, model versioning, experiment tracking, monitoring, and cloud deployment on AWS, GCP, or Azure.
Data Science & Analytics
Extract business insights from raw data using statistical modelling, predictive analytics, A/B testing, and interactive dashboards.
