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AI/ML Engineer with 5 years of experience developing, optimizing, and deploying machine learning and deep learning models using Python, SQL, TensorFlow, and PyTorch. Skilled in building scalable data pipelines, feature engineering, and rigorous model validation and tuning to improve performance. Experienced in production-grade deployments with FastAPI, Docker, and MLflow, with exposure to AWS and Azure. Hands-on with fine-tuning transformer-based LLMs (GPT, T5, Llama) and building RAG systems for intelligent automation across insurance, finance, and healthcare domains.…AI/ML Engineer with 5 years of experience developing, optimizing, and deploying machine learning and deep learning models using Python, SQL, TensorFlow, and PyTorch. Skilled in building scalable data pipelines, feature engineering, and rigorous model validation and tuning to improve performance. Experienced in production-grade deployments with FastAPI, Docker, and MLflow, with exposure to AWS and Azure. Hands-on with fine-tuning transformer-based LLMs (GPT, T5, Llama) and building RAG systems for intelligent automation across insurance, finance, and healthcare domains.

Akhila Sura

AI Engineer, Data Scientist, Data Engineer, +2

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AI/ML Engineer with 5 years of experience developing, optimizing, and deploying machine learning and deep learning models using Python, SQL, TensorFlow, and PyTorch. Skilled in building scalable data pipelines, feature engineering, and rigorous model validation and tuning to improve performance. Experienced in production-grade deployments with FastAPI, Docker, and MLflow, with exposure to AWS and Azure. Hands-on with fine-tuning transformer-based LLMs (GPT, T5, Llama) and building RAG systems for intelligent automation across insurance, finance, and healthcare domains.…AI/ML Engineer with 5 years of experience developing, optimizing, and deploying machine learning and deep learning models using Python, SQL, TensorFlow, and PyTorch. Skilled in building scalable data pipelines, feature engineering, and rigorous model validation and tuning to improve performance. Experienced in production-grade deployments with FastAPI, Docker, and MLflow, with exposure to AWS and Azure. Hands-on with fine-tuning transformer-based LLMs (GPT, T5, Llama) and building RAG systems for intelligent automation across insurance, finance, and healthcare domains.

Available to hire

AI/ML Engineer with 5 years of experience developing, optimizing, and deploying machine learning and deep learning models using Python, SQL, TensorFlow, and PyTorch. Skilled in building scalable data pipelines, feature engineering, and rigorous model validation and tuning to improve performance.

Experienced in production-grade deployments with FastAPI, Docker, and MLflow, with exposure to AWS and Azure. Hands-on with fine-tuning transformer-based LLMs (GPT, T5, Llama) and building RAG systems for intelligent automation across insurance, finance, and healthcare domains.

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Intermediate

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Language

Work Experience

AI/ML Engineer at UBER

February 1, 2024 - Present

Designed and implemented end-to-end ML solutions for driver demand forecasting and pricing workflows using Python, SQL, and AWS SageMaker, reducing manual intervention and improving operational efficiency. Built scalable data pipelines with PySpark/distributed processing and trained predictive models improving accuracy by 28% and reducing processing latency by 35%. Developed and optimized ML models using TensorFlow and PyTorch with feature engineering, hyperparameter tuning, and cross-validation. Fine-tuned transformer models (GPT, T5, Llama) using LoRA/PEFT to improve contextual accuracy and response quality by 40%+. Engineered RAG pipelines with LangChain and Pinecone/FAISS for document summarization and conversational AI, reducing manual review overhead. Integrated models into production using FastAPI and Docker, with CI/CD, MLflow tracking, and performance monitoring; collaborated on Responsible AI practices using SHAP/LIME/Fairlearn.

Machine Learning Engineer Intern at Capital One Financial

June 1, 2023 - December 1, 2023

Collaborated on ML models for fraud detection and credit risk analysis, increasing detection precision by 15%. Engineered and optimized preprocessing and feature engineering pipelines with Python and PySpark, reducing training time by 25% and improving pipeline efficiency. Supported AWS deployment with CI/CD for scalable enterprise integration. Performed validation and continuous monitoring to support fairness, interpretability, and Responsible AI compliance. Refined NLP systems using BERT-based supervised learning to improve customer-facing intent recognition accuracy. Documented workflows and results using MLflow for reproducibility and faster deployment cycles.

Machine Learning Engineer at Hyperlink InfoSystems

February 1, 2020 - June 1, 2022

Designed and implemented ML models for predictive analytics and classification across retail and logistics applications, improving accuracy by 18% through feature engineering and hyperparameter tuning. Built scalable ETL pipelines using Python and SQL to automate data cleaning and reduce manual effort by 30%. Deployed ML models using TensorFlow and PyTorch to enable real-time decision-making systems and maintain reliable performance across production client environments. Integrated ML components with REST APIs to ensure model performance, scalability, and stability in production. Conducted continuous model evaluation, retraining, and version control to mitigate model drift. Contributed to internal ML framework improvements to standardize experiment tracking and reduce onboarding time.

Machine Learning Engineer at Hyperlink InfoSyste m

February 1, 2020 - June 1, 2022

Designed and implemented ML models for predictive analytics and classification across retail and logistics domains, improving accuracy by 18% through feature engineering and tuning. Built scalable ETL pipelines using Python and SQL to automate data cleaning and reduce manual effort by 30%. Deployed models using TensorFlow and PyTorch for real-time decision making across production environments. Integrated ML components with REST APIs for stability and scalability in production. Performed continuous evaluation, retraining, and version control to mitigate model drift and maintain prediction quality. Contributed to internal ML framework improvements and standardized experiment tracking practices.