Results-driven AIML Engineer with over 3 years of experience in developing, deploying, and optimizing AI/ML solutions across enterprise and research-driven environments. Skilled in Large Language Models (LLMs), Generativ…
Results-driven AIML Engineer with over 3 years of experience in developing, deploying, and optimizing AI/ML solutions across enterprise and research-driven environments. Skilled in Large Language Models (LLMs), Generative AI, MLOps, and Data Engineering, with a strong track record at Molina Healthcare and HCLTech. Adept at leveraging cutting-edge tools and frameworks to deliver scalable, production-ready AI solutions. Proven ability to collaborate across global teams, drive innovation, and accelerate AI adoption.
Developed and deployed production-grade Generative AI solutions and LLM-based applications for enterprise and consumer products.
Worked extensively with LLaMA, GPT APIs, Hugging Face Transformers, and RAG pipelines for knowledge-intensive applications.
Designed and implemented MLOps pipelines using MLflow, Docker, Azure ML, and CI/CD, reducing deployment time by 40%.
Optimized deep learning models (CNNs, UNet, Autoencoders) for image/video analytics and multimodal AI applications.
Collaborated with cross-functional teams to integrate AI solutions with Microsoft Copilot, Ollama, and internal APIs, enhancing productivity tools.
Performed data engineering tasks including ETL, feature engineering, and preprocessing using Spark, Pandas, and NumPy.
Mentored junior engineers on best practices in ML lifecycle, model monitoring, and cloud deployment.
Implemented a knowledge retrieval augmented generation (RAG) pipeline that improved LLM response accuracy by 35%.
Contributed to Molina Healthcare's internal model deployment framework on Azure, reducing iteration cycles for new models.
Developed and deployed production-grade Generative AI solutions and LLM-based applications for enterprise and consumer products., Worked extensively with LLaMA, GPT APIs, Hugging Face Transformers, and RAG pipelines for knowledge-intensive applications., Designed and implemented MLOps pipelines using MLflow, Docker, Azure ML, and CI/CD, reducing deployment time by 40%., Optimized deep learning models (CNNs, UNet, Autoencoders) for image/video analytics and multimodal AI applications., Collaborated with cross-functional teams to integrate AI solutions with Microsoft Copilot, Ollama, and internal APIs, enhancing productivity tools., Performed data engineering tasks including ETL, feature engineering, and preprocessing using Spark, Pandas, and NumPy., Mentored junior engineers on best practices in ML lifecycle, model monitoring, and cloud deployment.
Developed and deployed a real-time anomaly detection system for pharmacy workflows, reducing delays and improving prescription verification efficiency.
Processed and engineered 115K+ records into scalable ML feature pipelines, increasing ETL speed by 30% and improving data reliability.
Trained deep learning models in PyTorch, boosting macro-F1 score by 8.9 points and minority-class recall by 15%.
Reduced false positives by 22% through model optimization, class balancing, and hyperparameter tuning.
Containerized ML training and inference pipelines using Docker and MLflow, increasing throughput by 11% and reducing queue delays by 18%.
Implemented automated model evaluation and validation pipelines to ensure production-grade ML deployment standards.
Developed and deployed a real-time anomaly detection system for pharmacy workflows, reducing delays and improving prescription verification efficiency., Processed and engineered 115K+ records into scalable ML feature pipelines, increasing ETL speed by 30% and improving data reliability., Trained deep learning models in PyTorch, boosting macro-F1 score by 8.9 points and minority-class recall by 15%., Reduced false positives by 22% through model optimization, class balancing, and hyperparameter tuning., Containerized ML training and inference pipelines using Docker and MLflow, increasing throughput by 11% and reducing queue delays by 18%., Implemented automated model evaluation and validation pipelines to ensure production-grade ML deployment standards.
Designed, trained, and deployed ML and deep learning models for enterprise-scale projects in healthcare, finance, and retail.
Built predictive models using PyTorch, TensorFlow, Scikit-learn for classification, regression, and anomaly detection.
Developed ETL pipelines and data preprocessing workflows using Pandas, NumPy, and Spark, ensuring clean and reliable datasets.
Implemented MLOps practices including Dockerized model deployment, MLflow tracking, and CI/CD integration on Azure & AWS.
Assisted in LLM and NLP projects, integrating Hugging Face Transformers for chatbots and document summarization tasks.
Worked with SQL and NoSQL databases for data storage, querying, and feature extraction.
Reduced model training time by 25% through optimized data pipelines and GPU utilization strategies.
Successfully deployed automated predictive analytics platform for client reporting and decision-making.
Designed, trained, and deployed ML and deep learning models for enterprise-scale projects in healthcare, finance, and retail., Built predictive models using PyTorch, TensorFlow, Scikit-learn for classification, regression, and anomaly detection., Developed ETL pipelines and data preprocessing workflows using Pandas, NumPy, and Spark, ensuring clean and reliable datasets., Implemented MLOps practices including Dockerized model deployment, MLflow tracking, and CI/CD integration on Azure & AWS., Assisted in LLM and NLP projects, integrating Hugging Face Transformers for chatbots and document summarization tasks., Worked with SQL and NoSQL databases for data storage, querying, and feature extraction.
The AI-first Recruitment Operating System that unifies and automates the entire hiring lifecycle for modern enterprises worldwide.
Dr Job FZ LLC. 2026 © All Rights Reserved
