About Me
A data engineer and AI/ML specialist with deep expertise in building robust machine learning systems, scalable data infrastructure, and modern MLOps solutions that drive real-world business impact.
Professional Journey
My work sits at the intersection of AI systems, data engineering, and production infrastructure, with a focus on building machine learning solutions that operate reliably at scale and drive real business outcomes.
I currently lead Data Engineering, AI, and MLOps initiatives at Droisys Inc., where I design and deploy large-scale computer vision, generative AI, and agentic systems for enterprise retail analytics. I work across the full lifecycle of AI systems - from data ingestion and annotation pipelines to multi-GPU model training, low-latency inference, and long-term operational monitoring - transforming experimental models into production-ready platforms trusted by business, sales, and operations teams.
My recent work has focused on high-performance computer vision systems, where I have architected multi-model YOLO-based pipelines with distributed and multi-GPU training. These systems support near real-time inference at scale and have delivered meaningful improvements in accuracy, latency, and training efficiency. I have also designed two-stage pipelines combining object detection, OCR, and transformer-based matching to solve complex recognition problems in unconstrained retail environments, enabling reliable insights from real-world data.
In parallel, I build GPU-accelerated inference platforms and modernize legacy systems into production-ready services. I also work on MLOps and LLM-powered systems, including agentic and retrieval-based architectures, with a focus on scalability and operational reliability.
Core Skills
Languages
AI/ML
Cloud & DevOps
Data
Frameworks
Areas of Expertise
Data Engineering
Scalable pipelines, real-time and batch data processing, data lakes, and cloud warehousing.
Machine Learning
End-to-end model development for computer vision, NLP, generative AI, and large-scale deployment.
Analytics
Business intelligence, advanced reporting, and sales analytics using modern visualization tools.
Software Engineering
Full-stack app design (React, Node, .NET), API development, and production automation.
Infrastructure
Cloud-native architecture on AWS/GCP, containerization (Docker/Kubernetes), and MLOps automation.
Big Data
Distributed systems, high-performance batch/stream processing with Spark, and scalable data solutions.
Resume
My professional journey in software engineering, AI, and machine learning
Professional Experience
Data Engineer
Led AI/ML system development for real-time shelf analytics and MLOps infrastructure overhaul across the company.
- Designed and implemented a real-time brand detection system using YOLOv7, achieving 25% higher accuracy and 80% faster training through multi-GPU optimization and advanced data augmentation techniques
- Engineered a scalable FastAPI-based REST service for real-time shelf analytics, processing 10,000+ images daily and contributing to 10-15% sales growth for retail clients
- Optimized model inference pipeline using NVIDIA Triton, reducing latency by 30% and enabling real-time processing of high-volume image streams
- Developed a generative AI pipeline leveraging Stable Diffusion and LoRA optimization, reducing ad campaign production time from weeks to hours (95% reduction)
- Created a document intelligence system using LangGraph and LangChain, enabling natural language querying of unstructured PDFs with 92% accuracy in information retrieval
- Modernized MLOps infrastructure with Kubernetes, MLflow, and DVC, reducing model training time by 60-70% while improving experiment tracking and reproducibility
- Built a high-throughput data processing pipeline using Apache Kafka, Airflow, and Spark, reducing data latency by 70% while handling 1M+ events per day
- Deployed and optimized on-premises LLM (DeepSeek) with FastAPI streaming, achieving sub-200ms response times for enterprise RAG applications
- Mentored junior engineers in ML engineering best practices, conducting bi-weekly knowledge sharing sessions on MLOps and LLM deployment strategies
Computer Programmer Analyst 1
Developed and implementing data engineering and ML techniques to solve business problems and drive customer value.
- Used state-of-the-art machine learning models and neural networks for solving object detection problems.
- Employed Python to develop data pipelines that efficiently assist in the building, training, and scaling of machine learning and AI solutions for business applications.
- Implemented best practices for data modeling, data quality, and data governance.
- Employed Python and Flask to develop efficient and scalable machine learning and AI solutions for business applications.
- Technologies: Python, PyTorch, YOLOv7, Flask, Docker, MongoDB Atlas, Apache Kafka, Airflow
MTS Software Engineer II
Worked on logging infrastructure and bug resolution in enterprise software systems.
- Developed end-to-end data protection and monitoring features for Snapcenter Plugin for VMware vSphere, contributing across full SDLC including design, development, testing, and production support
- Designed and implemented a scalable Audit Logging service using .NET and Entity Framework, delivering comprehensive system activity tracking and compliance reporting
- Led cross-functional collaboration to address over 3% of customer-reported bugs, significantly improving product reliability and customer satisfaction
- Spearheaded security compliance by managing Blackduck tooling, reducing vulnerability and license violations by 10%
- Authored technical documentation and conducted knowledge transfer sessions for product support and engineering teams, ensuring smooth feature adoption and release cycles
Education
Master of Science in Computer Science
Bachelor of Science in Computer Science
Technical Specializations
Get In Touch
I'm always open to discussing new opportunities, interesting projects, or just having a conversation about data engineering and AI/ML.