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AI/ML Engineer with 3+ years of experience building and deploying scalable machine learning and deep learning solutions across financial, healthcare, and enterprise domains. Proficient in Python, SQL, and advanced algorithms (XGBoost, LightGBM, CatBoost) with expertise in PyTorch, TensorFlow, and transformer-based NLP models (BERT, GPT, LLaMA). Skilled in Generative AI applications using LangChain, RAG pipelines, and vector databases (FAISS, Pinecone). Strong background in feature engineering, model evaluation, interpretability (SHAP, LIME), and hyperparameter tuning. Experienced in deploying production-ready ML systems with REST APIs, Docker, Kubernetes, MLflow, and CI/CD workflows on cloud platforms (AWS/GCP/Azure).…AI/ML Engineer with 3+ years of experience building and deploying scalable machine learning and deep learning solutions across financial, healthcare, and enterprise domains. Proficient in Python, SQL, and advanced algorithms (XGBoost, LightGBM, CatBoost) with expertise in PyTorch, TensorFlow, and transformer-based NLP models (BERT, GPT, LLaMA). Skilled in Generative AI applications using LangChain, RAG pipelines, and vector databases (FAISS, Pinecone). Strong background in feature engineering, model evaluation, interpretability (SHAP, LIME), and hyperparameter tuning. Experienced in deploying production-ready ML systems with REST APIs, Docker, Kubernetes, MLflow, and CI/CD workflows on cloud platforms (AWS/GCP/Azure).

Sai Pranav Thota

AI Engineer, AI Developer, Data Scientist, +2

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AI/ML Engineer with 3+ years of experience building and deploying scalable machine learning and deep learning solutions across financial, healthcare, and enterprise domains. Proficient in Python, SQL, and advanced algorithms (XGBoost, LightGBM, CatBoost) with expertise in PyTorch, TensorFlow, and transformer-based NLP models (BERT, GPT, LLaMA). Skilled in Generative AI applications using LangChain, RAG pipelines, and vector databases (FAISS, Pinecone). Strong background in feature engineering, model evaluation, interpretability (SHAP, LIME), and hyperparameter tuning. Experienced in deploying production-ready ML systems with REST APIs, Docker, Kubernetes, MLflow, and CI/CD workflows on cloud platforms (AWS/GCP/Azure).…AI/ML Engineer with 3+ years of experience building and deploying scalable machine learning and deep learning solutions across financial, healthcare, and enterprise domains. Proficient in Python, SQL, and advanced algorithms (XGBoost, LightGBM, CatBoost) with expertise in PyTorch, TensorFlow, and transformer-based NLP models (BERT, GPT, LLaMA). Skilled in Generative AI applications using LangChain, RAG pipelines, and vector databases (FAISS, Pinecone). Strong background in feature engineering, model evaluation, interpretability (SHAP, LIME), and hyperparameter tuning. Experienced in deploying production-ready ML systems with REST APIs, Docker, Kubernetes, MLflow, and CI/CD workflows on cloud platforms (AWS/GCP/Azure).

Available to hire

AI/ML Engineer with 3+ years of experience building and deploying scalable machine learning and deep learning solutions across
financial, healthcare, and enterprise domains. Proficient in Python, SQL, and advanced algorithms (XGBoost, LightGBM, CatBoost) with
expertise in PyTorch, TensorFlow, and transformer-based NLP models (BERT, GPT, LLaMA). Skilled in Generative AI applications using
LangChain, RAG pipelines, and vector databases (FAISS, Pinecone). Strong background in feature engineering, model evaluation,
interpretability (SHAP, LIME), and hyperparameter tuning. Experienced in deploying production-ready ML systems with REST APIs,
Docker, Kubernetes, MLflow, and CI/CD workflows on cloud platforms (AWS/GCP/Azure).

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Language

English

Fluent

Work Experience

AI/ML Engineer at Northern Trust

March 1, 2025 - Present

Designed a predictive model using Python, Scikit-learn, and XGBoost to identify chronic disease patients likely to require medical intervention, integrating data from EHR, claims, and wearables. Developed and deployed an ML-based document classification system using NLP techniques (spaCy, TF-IDF) to automatically suggest accurate tags for uploaded investment documents, reducing manual tagging effort by 60%. Implemented real-time online learning workflows with River, enabling instant model updates from user feedback and improving tag prediction accuracy by over 15% during pilot phase. Engineered secure REST APIs to integrate the tagging module into the Front Office Solutions platform. Built a continuous monitoring pipeline using MLflow to track model accuracy, tag acceptance rates, and user correction patterns, enabling proactive retraining when accuracy dropped below defined thresholds. Developed a Retrieval-Augmented Generation (RAG) pipeline using HuggingFace Transformers and LangCha

Machine Learning Engineer at Aplus Datalytics

January 1, 2021 - July 1, 2023

Engineered a market risk forecasting model using ARIMA, Prophet, and XGBoost to classify volatility across asset classes, sourcing time-series data via Pandas and automating ingestion with PostgreSQL. Collected, cleaned, and transformed historical stock market time-series data using Python, Pandas, and NumPy, ensuring high-quality inputs for predictive modeling. Engineered technical indicators such as RSI, MACD, and moving averages to create robust feature sets for model training, improving signal accuracy by 18%. Developed and optimized machine learning models using scikit-learn, LightGBM, and XGBoost to forecast short-term stock price movements with associated confidence scores. Designed and executed backtesting frameworks to evaluate model performance on historical data, comparing cumulative returns against benchmark indices like NIFTY 50. Built and deployed REST API endpoints to deliver trade signals in real-time. Built a Generative AI–powered financial news sentiment and summari