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I am an AI & Machine Learning Engineer with over six years of experience designing, deploying, and optimizing advanced AI solutions across healthcare, research, and enterprise domains. I specialize in large language models, generative AI, federated learning, and MLOps, with a solid background in transforming research innovations into production-grade AI systems. My expertise includes working extensively with frameworks like TensorFlow, PyTorch, Hugging Face, and cloud platforms such as AWS SageMaker and Azure ML. I am passionate about building scalable AI infrastructures, ensuring compliance with healthcare regulations, and implementing explainable AI models to enhance transparency and trust.…I am an AI & Machine Learning Engineer with over six years of experience designing, deploying, and optimizing advanced AI solutions across healthcare, research, and enterprise domains. I specialize in large language models, generative AI, federated learning, and MLOps, with a solid background in transforming research innovations into production-grade AI systems. My expertise includes working extensively with frameworks like TensorFlow, PyTorch, Hugging Face, and cloud platforms such as AWS SageMaker and Azure ML. I am passionate about building scalable AI infrastructures, ensuring compliance with healthcare regulations, and implementing explainable AI models to enhance transparency and trust.

Jaykumar Kotiya

AI Engineer, Developer, AI Developer, +2

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I am an AI & Machine Learning Engineer with over six years of experience designing, deploying, and optimizing advanced AI solutions across healthcare, research, and enterprise domains. I specialize in large language models, generative AI, federated learning, and MLOps, with a solid background in transforming research innovations into production-grade AI systems. My expertise includes working extensively with frameworks like TensorFlow, PyTorch, Hugging Face, and cloud platforms such as AWS SageMaker and Azure ML. I am passionate about building scalable AI infrastructures, ensuring compliance with healthcare regulations, and implementing explainable AI models to enhance transparency and trust.…I am an AI & Machine Learning Engineer with over six years of experience designing, deploying, and optimizing advanced AI solutions across healthcare, research, and enterprise domains. I specialize in large language models, generative AI, federated learning, and MLOps, with a solid background in transforming research innovations into production-grade AI systems. My expertise includes working extensively with frameworks like TensorFlow, PyTorch, Hugging Face, and cloud platforms such as AWS SageMaker and Azure ML. I am passionate about building scalable AI infrastructures, ensuring compliance with healthcare regulations, and implementing explainable AI models to enhance transparency and trust.

Available to hire

I am an AI & Machine Learning Engineer with over six years of experience designing, deploying, and optimizing advanced AI solutions across healthcare, research, and enterprise domains. I specialize in large language models, generative AI, federated learning, and MLOps, with a solid background in transforming research innovations into production-grade AI systems.

My expertise includes working extensively with frameworks like TensorFlow, PyTorch, Hugging Face, and cloud platforms such as AWS SageMaker and Azure ML. I am passionate about building scalable AI infrastructures, ensuring compliance with healthcare regulations, and implementing explainable AI models to enhance transparency and trust.

Skills

Experience Level

Expert

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Expert

Work Experience

AI Engineer at CitiusTech, USA

July 1, 2024 - Present

Architected LLM-powered clinical decision support systems using GPT, LLaMA, Falcon, LangChain, FAISS, and Hugging Face Transformers, improving diagnostic accuracy by 32% across multi-specialty use cases. Deployed RAG pipelines on AWS SageMaker, Azure Machine Learning, and Kubernetes with vector databases for secure retrieval. Engineered multi-modal AI frameworks combining NLP, computer vision, and time-series forecasting to predict patient outcomes, achieving 24% higher accuracy. Orchestrated MLOps pipelines with Kubeflow, MLFlow, TFX, Jenkins, Docker, and Terraform, reducing production deployment time by 40%. Applied Explainable AI techniques ensuring compliance with FDA and healthcare AI transparency guidelines. Optimized edge AI and LLM inference models with ONNX Runtime, TensorRT, quantization, and pruning, lowering model size by 35% and reducing cloud costs by 30%.

Artificial Intelligence Research Engineer at Hexaware Technologies, India

July 1, 2022 - September 4, 2025

Researched generative AI, reinforcement learning (PPO, A3C), and transfer learning developing AI-driven decision-making agents, improving optimization performance by 22% in simulations. Developed hyperparameter optimization frameworks using JAX, Ray Tune, Optuna, and Hyperopt, achieving 18% uplift on benchmarks. Prototyped privacy-preserving federated learning frameworks with TensorFlow Federated and AWS Secure Enclaves, meeting GDPR and HIPAA compliance. Enhanced computer vision pipelines improving defect detection accuracy by 25%. Designed AutoML workflows accelerating model prototyping by 30%. Published AI research integrating Explainable AI to improve interpretability and promote ethical AI adoption.

Machine Learning Engineer at Encode Testers, India

December 31, 2019 - September 4, 2025

Developed classification, regression, and ensemble models using Scikit-learn, XGBoost, LightGBM, CatBoost improving predictive performance by 21%. Automated ETL and feature engineering pipelines using Pandas, Dask, Apache Spark, and Airflow reducing data prep cycle by 40%. Built real-time ML APIs and microservices deployed via Docker and Kubernetes achieving sub-50ms latency and 33% higher throughput. Performed hyperparameter tuning with Optuna and Bayesian optimization improving accuracy by 15% while reducing inference cost by 18%. Designed BI dashboards with Tableau, Power BI, and Plotly Dash delivering actionable insights accelerating stakeholder decisions by 20%. Established continuous training pipelines with MLFlow, DVC, and GitHub Actions improving long-term model stability by 25%.