Skills
Methodologies: SDLC, Agile, MLOps, Model Lifecycle Management, Cross-Functional Collaboration
Core Competencies: Machine Learning, Deep Learning, Model Deployment, MLOps, GPU Optimization, Computer Vision
Programming: Python, C++, SQL
Frameworks & Libraries: TensorFlow, PyTorch, Scikit-learn, Keras, XGBoost, LightGBM
Generative AI & NLP: Large Language Models, Hugging Face Transformers, BERT, Prompt Engineering, LangChain
GPU Acceleration: CUDA, TensorRT, Quantization, Inference Optimization
Distributed Computing & Data Engineering: Apache Spark, Airflow, Feature Engineering, ETL Pipelines
Cloud & Deployment: AWS SageMaker, Google Cloud AI Platform, Docker, Kubernetes, MLflow, CI/CD Pipelines,
Databases & Storage: MySQL, PostgreSQL, MongoDB
Visualization & Reporting: Tableau, Power BI, Matplotlib, Seaborn
Tools: Git, Jupyter Notebook, FastAPI, Flask
About
AI/ML Engineer with 5.6+ years of experience delivering production-grade machine learning and GenAI systems in regulated finance and
healthcare. Specialized in RAG-based LLM platforms, high-throughput model serving, and cost-efficient RLHF pipelines using Azure OpenAI,
vLLM, Kubernetes, and MLflow, driving up to 24× throughput gains and 75% cost reduction. Strong background in deep learning, NLP, and
multimodal modeling, with hands-on ownership of end-to-end ML pipelines from data engineering through deployment and governance-ready
production
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