About sai
Senior AI/ML Engineer | Generative AI | LLMs | MLOps | Cloud AI I am an AI/ML Engineer with 5+ years of experience designing, building, and deploying scalable machine learning and Generative AI solutions that solve complex business problems. My expertise spans the entire ML lifecycle—from data engineering and model development to MLOps, deployment, monitoring, and continuous optimization. I specialize in developing production-grade AI systems using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), NLP, Computer Vision, and predictive analytics. I have hands-on experience building enterprise AI solutions with technologies such as LangChain, GPT, BERT, LLaMA, Azure ML, AWS SageMaker, Docker, Kubernetes, and MLflow. Throughout my career, I have: * Built and deployed scalable ML and Generative AI applications on Azure and AWS cloud platforms * Developed RAG-based knowledge management systems with semantic search capabilities * Fine-tuned transformer models for NLP tasks including classification and summarization * Designed end-to-end MLOps pipelines for automated training, deployment, monitoring, and retraining * Implemented real-time model monitoring and drift detection frameworks to ensure model reliability * Built computer vision solutions using YOLO and deep learning architectures for real-time inference * Developed predictive analytics and customer segmentation models using advanced machine learning techniques Core Expertise: Generative AI & LLMs (GPT, BERT, LLaMA) Retrieval-Augmented Generation (RAG) & LangChain Machine Learning & Deep Learning NLP & Semantic Search Computer Vision & Real-Time Inference MLOps, CI/CD, Docker, Kubernetes, MLflow Azure ML, AWS SageMaker, Cloud-Native AI Solutions Python, SQL, FastAPI, MongoDB, PostgreSQL I am passionate about leveraging AI to create intelligent, scalable, and impactful solutions that drive business value and innovation. I enjoy collaborating with cross-functional teams, mentoring peers, and staying at the forefront of advancements in AI, machine learning, and Generative AI technologies. I'm always open to connecting with AI professionals, technology leaders, and organizations building the next generation of intelligent systems.
Key Skills
Experience
Senior Ai / Ml Engineer
CurrentDxc Technology
Designed and deployed production-grade machine learning systems on Azure (Azure ML, AKS, Blob Storage), ensuring high availability and fault tolerance for enterprise data applications. Built and maintained end-to-end MLOps pipelines using Airflow, Docker, and Kubernetes to automate model training, validation, deployment, and safe rollback across development, staging, and production environments. Developed Generative AI solutions for enterprise knowledge management, implementing RAG (Retrieval-Augmented Generation) with LangChain and MongoDB as the vector store for efficient semantic search over large-scale unstructured documents. Fine-tuned transformer models (BERT, LLaMA) for NLP tasks such as classification and summarization, achieving accuracy above 90% while reducing inference latency and compute usage through optimization techniques. Created RESTful inference APIs with integrated CI/CD pipelines, reducing the time from model approval to production deployment and enabling seamless integration with downstream enterprise systems. Engineered scalable data ingestion and feature engineering pipelines, incorporating schema validation, data quality checks, and reproducibility, with MongoDB used for flexible storage of intermediate and feature data. Implemented real-time model performance monitoring and data drift detection, enabling automated alerts and scheduled retraining to maintain prediction reliability in dynamic production environments.
Machine Learning Engineer
Infinite Infolab
* Developed and deployed predictive modeling and customer segmentation solutions using ensemble techniques (XGBoost, LightGBM), improving business decision accuracy. * Engineered real-time computer vision pipelines leveraging YOLO and EfficientNet architectures, optimized for low-latency inference. * Designed deep learning-based anomaly detection systems (LSTM, Autoencoders) for high-frequency data streams, improving anomaly detection precision by 25%. * Automated large-scale data preprocessing and feature engineering workflows, reducing pipeline latency and enhancing model consistency. * Delivered interactive BI dashboards using Power BI and Tableau, translating complex ML outputs into actionable business insights. * Implemented model lifecycle management frameworks, including version control, retraining strategies, and containerized deployments using Docker and AWS. * Collaborated cross-functionally to ensure model scalability, interpretability, and compliance with enterprise standards.
Education
Gannon University
Masters
Kluniversity
Bachelors
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Common Questions
What is sai's expertise?
sai specializes in Fine-Tuning Engineer, with expertise in aws sagemaker, llm fine tuning, machine learning, microsoft power apps, microsoft power bi.
Where is sai located?
sai is based in coraopolis, pennsylvania, united states.
How much experience does sai have?
sai has 7+ years of professional experience.
How can I contact sai?
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