Senior AI/ML Engineer with GenAI & MLOps Expertise

SRIKANTHREDDY M

********@**********.*** +1-667-***-**** Plainsboro, NJ LinkedIn GitHub

SUMMARY

AI/ML Engineer and Data Analyst with 5+ years of experience delivering data-driven, cloud-native, and GenAI solutions across technology, fintech, automotive/industrial AI, and financial/trading & risk analytics domains. Proven expertise in machine learning, deep learning, NLP, and Generative AI (LLMs, RAG, LangChain) to design predictive models, automate workflows, and optimize decision-making. Skilled in building end-to-end pipelines, model deployment, and MLOps using AWS, GCP, Azure, Docker, and Kubernetes, with strong proficiency in Python, SQL, and advanced statistical modeling. Adept at developing dashboards and collaborating with cross-functional teams, with a track record of improving operational efficiency, compliance readiness, and business impact. EDUCATION

Master of Science in Information Systems Technology (STEM) Wilmington University, New Castle, DE May 2025 EXPERIENCE

State Street, USA AI/ML Engineer (SME) Oct 2024 – Present

Managed AI/ML-driven financial technology projects using Agile methodologies, ensuring on-time delivery, cross-functional collaboration, and measurable improvements in client satisfaction and portfolio performance.

Developed and trained predictive AI/ML models in AWS SageMaker, integrating LangChain Chains and Agents, LLMs (GPT-3.5/4, Claude), and advanced prompt engineering to automate trade settlement scheduling and exception management, improving operational efficiency by 35%.

Leveraged open-source routing APIs and LangChain APIs to map client feedback and transaction queries to resolution workflows using embedding similarity; implemented LLM-powered clustering algorithms to group recurring transaction anomalies, reducing manual intervention by 30%.

Designed and executed large-scale data ingestion, preprocessing, and cleansing workflows in SQL for 50,000+ daily trade, compliance, and risk transaction records, enhancing data quality and model readiness for predictive risk and settlement forecasting models.

Led the development of an AI-driven compliance monitoring initiative, applying SageMaker, Redshift, and LangChain RAG pipelines to predict anomalies in trade compliance and liquidity reporting; the solution is currently under review for enterprise-wide deployment.

Built and deployed LLM-powered NLP pipelines for financial document analysis, risk report summarization, and regulatory topic clustering using Hugging Face (BERT, RoBERTa), containerized with Docker and deployed on AWS/GCP Kubernetes clusters for scalability.

Engineered Retrieval-Augmented Generation (RAG) pipelines with Amazon Bedrock, Redshift, and LangChain to enhance LLM outputs with structured portfolio, compliance, and financial datasets, improving accuracy, audit readiness, and decision support in enterprise intelligence systems.

KPIT, India AI/ML Engineer May 2021 – Jul 2023

Designed, developed, and deployed machine learning models (classification, regression, clustering, NLP, deep learning) and computer vision solutions (OpenCV, CNNs, YOLO, ResNet) for automotive and industrial use cases, improving detection and prediction accuracy by 25–30%.

Built and optimized end-to-end ML pipelines (data collection, preprocessing, feature engineering, training, validation, deployment) using Python, TensorFlow, PyTorch, Scikit-learn, and Spark, reducing model training time by 20%.

Implemented predictive maintenance solutions by analyzing IoT sensor data streams, enabling early fault detection and reducing automotive downtime by 15%.

Deployed scalable ML models on cloud platforms (AWS) using Docker and Kubernetes, optimizing performance and reducing inference latency by 35%.

Conducted NLP projects (intent classification, text mining, chatbots) using spaCy, NLTK, Word2Vec, and automated model evaluation & hyperparameter tuning with Python/Bash, reducing manual effort by 40%.

Created interactive dashboards and visualizations in Tableau, Power BI, and Matplotlib to communicate insights, collaborated with cross functional teams to align KPIs, and contributed to AI/ML research, PoCs, and whitepapers. HCL Tech, India Data Analyst Jan 2019 – April 2021

Partnered with cross-functional teams (trading ops, compliance, risk, IT) to resolve monthly data issues, maintain schema updates, and apply advanced analytics and automation for portfolio monitoring, compliance reporting, and risk workflows ensuring 95% SLA adherence, audit readiness, and fewer operational delays.

Designed and deployed interactive dashboards in Tableau and Power BI to visualize portfolio performance, compliance KPIs, risk exposure, and client profitability, accelerating decision-making by and supporting multiple business units.

Performed data wrangling, cleansing, and EDA on weekly client account and transaction records, detecting anomalous trading patterns, risk concentration, and underperforming segments to enable predictive risk modeling and deliver cost optimization.

Automated 50+ recurring financial/operational reports using Python (Pandas, NumPy), SQL, and Excel, implemented data validation rules for 1M+ monthly records, and optimized complex SQL queries, improving data quality, retrieval speed, and reporting efficiency. SKILLS

Programming & Methodologies: SDLC, Agile, Waterfall Python, SQL, R, C, C++, Java, MATLAB, HTML, CSS, JavaScript, TypeScript Frameworks & Libraries: TensorFlow, PyTorch, Keras, Scikit-learn, NumPy, Pandas, Matplotlib, Seaborn, OpenCV Machine Learning & Deep Learning: Linear & Logistic Regression, Random Forest, SVM, XGBoost, Decision Trees, Clustering, PCA, Lasso, Ridge, Time Series Forecasting, A/B Testing, Hypothesis Testing, Bayesian Inference, CNN, RNN, LSTM, Autoencoders, Attention Mechanism, Transfer Learning Generative AI & LLMs: Large Language Models (GPT, LLaMA, Claude), LangChain (Chains, Agents, Tools), Retrieval-Augmented Generation (RAG), Hugging Face Transformers, Prompt Engineering, Tokenization (BPE, WordPiece), RLHF, Semantic Search, Embedding Models NLP Tools & Techniques: NLTK, spaCy, BERT, RoBERTa, GPT-2/3, Text Summarization, Named Entity Recognition, Text Classification, Semantic Similarity

MLOps & Deployment: Docker, Kubernetes, Jenkins, Git/GitHub/GitLab, CI/CD (CodePipeline, CodeBuild, CodeDeploy), Terraform, Flask, MLflow, Weights & Biases, Evidently AI

Cloud Platforms & Data Engineering: AWS (SageMaker, Bedrock, EC2, Lambda, Comprehend, Polly, Transcribe, Rekognition, Glue, Redshift, RDS, S3, IAM, CloudFormation, CloudWatch, X-Ray), Azure ML, GCP (BigQuery, GKE), Apache Airflow, Spark (PySpark), Databricks Databases & Vector DBs: PostgreSQL, MySQL, Oracle, SQL Server, MongoDB, Pinecone, FAISS, ChromaDB Visualization Tools: Tableau, Power BI, Excel, Matplotlib, Seaborn Development Soft Skills: Jupyter Notebook, PyCharm, Visual Studio Code Windows, Linux, MacOS Analytical Thinking, Communication, Cross-functional Collaboration, Problem Solving, Leadership

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