AI/ML Engineer with 3+ years of experience designing, developing, and deploying scalable machine learning and Generative AI solutions across enterprise environments. Experienced in building production-ready AI applications using Large Language Models (LLMs), Retrieval Augmented Generation (RAG), AI agents, NLP, deep learning, and cloud-native MLOps pipelines. Skilled in developing end-to-end ML workflows from data engineering and feature engineering to model training, deployment, monitoring, and continuous optimization using Python, PyTorch, TensorFlow, LangChain, Hugging Face, AWS, Docker, and Kubernetes. Strong background in developing intelligent automation solutions, integrating foundation models through APIs, optimizing inference performance, and delivering AI-driven business solutions that improve operational efficiency, prediction accuracy, and user experience. Adept at collaborating with crossfunctional engineering, product, and data science teams to deliver secure, scalable, and production-grade AI systems following modern software engineering and MLOps best practices.

Sonali Kandepola

AI/ML Engineer with 3+ years of experience designing, developing, and deploying scalable machine learning and Generative AI solutions across enterprise environments. Experienced in building production-ready AI applications using Large Language Models (LLMs), Retrieval Augmented Generation (RAG), AI agents, NLP, deep learning, and cloud-native MLOps pipelines. Skilled in developing end-to-end ML workflows from data engineering and feature engineering to model training, deployment, monitoring, and continuous optimization using Python, PyTorch, TensorFlow, LangChain, Hugging Face, AWS, Docker, and Kubernetes. Strong background in developing intelligent automation solutions, integrating foundation models through APIs, optimizing inference performance, and delivering AI-driven business solutions that improve operational efficiency, prediction accuracy, and user experience. Adept at collaborating with crossfunctional engineering, product, and data science teams to deliver secure, scalable, and production-grade AI systems following modern software engineering and MLOps best practices.

Available to hire

AI/ML Engineer with 3+ years of experience designing, developing, and deploying scalable machine learning and Generative AI solutions across enterprise
environments. Experienced in building production-ready AI applications using Large Language Models (LLMs), Retrieval Augmented Generation (RAG), AI
agents, NLP, deep learning, and cloud-native MLOps pipelines. Skilled in developing end-to-end ML workflows from data engineering and feature engineering
to model training, deployment, monitoring, and continuous optimization using Python, PyTorch, TensorFlow, LangChain, Hugging Face, AWS, Docker, and
Kubernetes. Strong background in developing intelligent automation solutions, integrating foundation models through APIs, optimizing inference performance,
and delivering AI-driven business solutions that improve operational efficiency, prediction accuracy, and user experience. Adept at collaborating with crossfunctional engineering, product, and data science teams to deliver secure, scalable, and production-grade AI systems following modern software engineering
and MLOps best practices.

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Work Experience

AI/ML Engineer at Scale AI
January 1, 2026 - Present
Developed Generative AI applications using Python, LangChain, OpenAI GPT models, and FastAPI to automate document analysis, knowledge retrieval, and conversational workflows, reducing manual effort by 35% while improving response consistency across internal business teams. Built RAG pipelines using Pinecone, FAISS, and embedding models to integrate enterprise knowledge sources, improving contextual response accuracy by 28% and reducing hallucinations during production testing. Designed reusable prompt engineering frameworks with function calling, structured outputs, and context management to increase response reliability by 30% across multiple enterprise AI use cases. Assisted with ML pipelines using Python, PyTorch, Scikit-learn, and MLflow for preprocessing, feature engineering, training, and experiment tracking, reducing development time by 24%. Supported deployment on AWS using SageMaker, Lambda, Docker, Kubernetes, and CI/CD pipelines, improving deployment efficiency by 30% while
Machine Learning Engineer at Fractal Analytics
July 1, 2021 - July 1, 2024
Designed and deployed end-to-end ML solutions using Python, Scikit-learn, XGBoost, TensorFlow, and SQL for customer segmentation, forecasting, and predictive analytics, improving accuracy by 26% across enterprise engagements. Built scalable ETL and feature engineering workflows using Apache Spark, Pandas, Airflow, and SQL to process 40M+ records, reducing data preparation time by 38%. Implemented NLP and document intelligence solutions using Hugging Face Transformers (BERT, Sentence Transformers) for classification, semantic search, and document understanding, improving text prediction performance by 24%. Evaluated models with Precision, Recall, F1, ROC-AUC, and cross-validation to improve production stability by 20%. Built REST APIs with FastAPI and Flask for real-time inference, using Docker and cloud deployment workflows to reduce latency by 27%. Automated training, experiment tracking, and deployment using MLflow, Git, Jenkins, and Docker, improving release consistency by 30% and s

Education

Master of Technology Management with Computer Science Concentration at Avila University
September 1, 2024 - December 1, 2025
Btech in Computer Science at Sridevi Women’s Engineering College
August 1, 2019 - September 1, 2023
Master of Technology Management with Computer Science Concentration at Avila University
September 1, 2024 - December 1, 2025
Btech in Computer Science at Sridevi Women’s Engineering College
August 1, 2019 - September 1, 2023

Qualifications

Add your qualifications or awards here.

Industry Experience

Computers & Electronics, Software & Internet, Professional Services