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I'm Pushan Prakash Murthy, an AI/ML Engineer with 3+ years of experience designing and deploying machine learning and generative AI solutions in production. I specialize in LLM fine-tuning, RAG development, and automated MLOps with MLflow, Airflow, and Kubernetes. I'm proficient in PyTorch, TensorFlow, and Spark for scalable model training and data processing. I focus on improving inference speed, accuracy, and cost efficiency while delivering AI systems aligned with measurable business impact.…I'm Pushan Prakash Murthy, an AI/ML Engineer with 3+ years of experience designing and deploying machine learning and generative AI solutions in production. I specialize in LLM fine-tuning, RAG development, and automated MLOps with MLflow, Airflow, and Kubernetes. I'm proficient in PyTorch, TensorFlow, and Spark for scalable model training and data processing. I focus on improving inference speed, accuracy, and cost efficiency while delivering AI systems aligned with measurable business impact.

Pushan Prakash Murthy

AI Engineer, Data Scientist, DevOps Developer, +2

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I'm Pushan Prakash Murthy, an AI/ML Engineer with 3+ years of experience designing and deploying machine learning and generative AI solutions in production. I specialize in LLM fine-tuning, RAG development, and automated MLOps with MLflow, Airflow, and Kubernetes. I'm proficient in PyTorch, TensorFlow, and Spark for scalable model training and data processing. I focus on improving inference speed, accuracy, and cost efficiency while delivering AI systems aligned with measurable business impact.…I'm Pushan Prakash Murthy, an AI/ML Engineer with 3+ years of experience designing and deploying machine learning and generative AI solutions in production. I specialize in LLM fine-tuning, RAG development, and automated MLOps with MLflow, Airflow, and Kubernetes. I'm proficient in PyTorch, TensorFlow, and Spark for scalable model training and data processing. I focus on improving inference speed, accuracy, and cost efficiency while delivering AI systems aligned with measurable business impact.

Available to hire

I’m Pushan Prakash Murthy, an AI/ML Engineer with 3+ years of experience designing and deploying machine learning and generative AI solutions in production. I specialize in LLM fine-tuning, RAG development, and automated MLOps with MLflow, Airflow, and Kubernetes. I’m proficient in PyTorch, TensorFlow, and Spark for scalable model training and data processing. I focus on improving inference speed, accuracy, and cost efficiency while delivering AI systems aligned with measurable business impact.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Language

English

Fluent

Work Experience

AI Engineer at C3.ai

February 1, 2024 - Present

Built and deployed GPT-based and RAG-enabled AI systems using FAISS and Pinecone, improving contextual retrieval accuracy and reducing information latency across enterprise applications. Designed end-to-end MLOps pipelines with MLflow, Airflow, and Kubernetes to automate retraining, versioning, and deployment, reducing manual effort and ensuring reproducibility. Fine-tuned transformer architectures (BERT, GPT-J) on PyTorch, lowering inference latency and AWS SageMaker compute costs through model optimization. Implemented Spark/Databricks data processing to accelerate ETL cycles, enabling near real-time model refresh. Containerized FastAPI microservices with Docker and deployed on SageMaker endpoints with high uptime while handling millions of inferences daily. Integrated SHAP and LIME for explainability in dashboards to support audit and governance. Collaborated with cross-functional teams in Agile sprints to deliver AI features faster and align with business outcomes.

ML Engineer at Fractal Analytics

July 1, 2022 - October 24, 2025

Delivered predictive models using XGBoost and LightGBM, increasing forecast accuracy and identifying significant cost reduction opportunities. Automated feature engineering pipelines with Python and SQL, speeding data preparation and ensuring input quality across model training workflows. Developed CNN/RNN solutions with TensorFlow and Keras for image and sequence analysis, improving model performance over baselines. Deployed Flask-based APIs in Docker, integrating predictive models into enterprise BI tools and reducing insight delivery time. Applied hyperparameter tuning with Optuna and GridSearchCV to improve generalization. Implemented drift detection and bias monitoring using MLflow and Prometheus to reduce production model failure incidents and ensure long-term reliability. Collaborated with data engineers and analysts to translate modeling outputs into clear business KPIs and ROI.

AI Engineer at C3.ai

February 1, 2024 - Present

Built and deployed GPT-based and RAG-enabled AI systems using FAISS and Pinecone, improving contextual retrieval accuracy by 38% and reducing information latency across enterprise applications. Designed end-to-end MLOps pipelines with MLflow, Airflow, and Kubernetes to automate model retraining, versioning, and deployment, cutting manual effort by 55% and ensuring reproducibility. Fine-tuned transformer architectures (BERT, GPT-J) on PyTorch, reducing inference latency by 27% and lowering AWS SageMaker compute costs by 22% through model optimization. Created data processing workflows in Spark and Databricks that shortened ETL cycles from two hours to fifteen minutes, enabling near real-time model refresh and analytics. Containerized FastAPI microservices with Docker, deployed on SageMaker endpoints, achieving 99.99% uptime while processing over one million daily inference requests across client platforms. Integrated explainability tools such as SHAP and LIME into production dashboards,

ML Engineer at Fractal Analytics

July 1, 2022 - October 24, 2025

Delivered predictive models using XGBoost and LightGBM, increasing forecast accuracy by 42% and helping clients identify $5M in annual cost reduction opportunities. Automated feature engineering pipelines with Python and SQL, improving data preparation speed by 60% and ensuring consistent input quality across all model training workflows. Developed deep learning solutions (CNN, RNN) with TensorFlow and Keras for image and sequence analysis, improving model performance by 28% over baselines. Deployed Flask-based APIs in Docker containers, integrating predictive models into enterprise BI tools and cutting insight delivery time by 70%. Applied hyperparameter tuning using Optuna and GridSearchCV, improving generalization accuracy by 21% while maintaining stable performance. Implemented drift detection and bias monitoring using MLflow and Prometheus, reducing production model failure incidents by 40% and ensuring long-term reliability.