Dhana santhosh (J) R.
AI/ML Engineer
Experience
Nov 2024 - Present
1 year 1 monthUnited States
AI/ML Engineer
Morgan Stanley
- Designed and deployed Generative AI and Large Language Model (LLM) solutions using LangChain, Hugging Face Transformers, and PyTorch, enabling automation of financial document analysis and client communication workflows.
- Developed RAG pipelines integrating vector databases to enhance context retrieval for regulatory, compliance, and investment applications.
- Built predictive and classification models using LightGBM, XGBoost, and Logistic Regression to identify trading anomalies, customer churn patterns, and cross-sell opportunities, improving model accuracy and interpretability.
- Designed and implemented time-series forecasting frameworks using Azure Machine Learning, AutoML, and XGBoost Regressor to predict revenue volatility, liquidity ratios, and credit risk exposure, enabling proactive financial planning and capital allocation.
- Developed serverless ETL workflows leveraging Azure Data Factory, Azure Synapse Analytics, and Data Lake Storage Gen2 to orchestrate ingestion, transformation, and validation of high-volume financial transaction and market feed data.
- Deployed and monitored containerized ML pipelines on Azure Kubernetes Service (AKS) with integrated Azure DevOps CI/CD for automated retraining, versioning, and real-time model scoring.
- Integrated Azure Monitor, Log Analytics, and Application Insights for end-to-end observability and model performance tracking across distributed environments.
- Integrated speech recognition, NLP, and computer vision modules into multi-modal AI assistants for internal analytics, enhancing productivity and accessibility across research teams.
- Collaborated with risk, data, and compliance teams to enforce model governance, explainability (XAI), and security frameworks, ensuring adherence to HIPAA, SOC 2, and internal audit standards.
Jan 2021 - Jul 2023
2 years 7 monthsIndia
AI/ML Engineer
Rlogical Techsoft Pvt. Ltd
- Designed, developed, and deployed machine learning classification models using Python, scikit-learn, and XGBoost to predict customer churn and drive retention strategies through actionable business insights.
- Engineered robust feature engineering pipelines with Pandas and NumPy, leveraging encoding, binning, and dimensionality reduction (PCA) techniques to optimize data quality and improve model performance.
- Automated ETL workflows and data ingestion pipelines using Apache Airflow, SQL, and AWS S3, ensuring high data integrity, reduced latency, and consistent pipeline reliability for analytics applications.
- Enhanced model explainability through SHAP and LIME frameworks, translating complex ML outputs into interpretable insights for stakeholders and supporting data-driven decision-making.
- Optimized model accuracy and generalization using hyperparameter tuning frameworks like Optuna and GridSearchCV, implementing k-fold validation to prevent overfitting and ensure robust predictions.
- Deployed scalable ML solutions using Docker containers and AWS SageMaker, integrating Flask/FastAPI for real-time inference APIs and automating CI/CD processes through Jenkins and GitHub Actions.
- Implemented MLOps best practices by integrating MLflow for experiment tracking, model registry, and lifecycle management, streamlining collaboration between data science and DevOps teams.
- Collaborated cross-functionally with product managers and data engineers to align ML model outputs with CRM, marketing, and financial objectives, improving personalization and targeting efficiency.
- Applied time-series forecasting models (ARIMA, Prophet, XGBoost Regressor) for revenue trend prediction and financial risk analysis, supporting business planning and operational optimization.
- Ensured data privacy and compliance with GDPR and HIPAA standards by anonymizing PII and implementing secure cloud-based data handling practices in production ML environments.
Summary
AI/ML Engineer with around 4 years of experience designing, developing, and deploying scalable machine learning, deep learning, and Generative AI solutions across financial and enterprise domains. Skilled in building and fine-tuning LLMs and RAG pipelines using LangChain, Hugging Face Transformers, and vector databases such as FAISS, Pinecone, and Chroma. Proficient in Python, scikit-learn, XGBoost, LightGBM, and SHAP for predictive modeling, time-series forecasting, and explainable AI. Experienced in architecting end-to-end ETL pipelines using Apache Airflow, SQL, and AWS services to enable real-time analytics. Adept at deploying AI models through Docker, Kubernetes, and CI/CD pipelines within AWS and Azure environments. Strong collaborator skilled at aligning AI initiatives with business goals to enhance forecasting accuracy, automation, and decision-making efficiency.
Languages
English
AdvancedGerman
IntermediateJapanese
IntermediateEducation
University of Maryland, College Park
Master’s · Robotics · College Park, United States
SRM Institute of Science and Technology
Bachelor’s · Mechatronics · Chennai, India
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