# Tejaswi J > AI/ML Engineer at HSBCAI-Driven Fraud Detection, Cloud Data Platforms, End-to-End ML Pipelines Financial Services Innovation Location: Marlton, New Jersey, United States Profile: https://flows.cv/tejaswij AI/ML Engineer with 4+ years of experience designing and deploying enterprise-scale AI solutions in financial services. Skilled in building end-to-end ML pipelines, real-time fraud detection, NLP-driven risk analytics, and generative AI applications using Python, PySpark, SQL, Transformers, LangChain, and AWS Bedrock. Experienced in MLOps, cloud-native architectures, and scalable data systems, delivering measurable business impact through automated retraining, explainable AI, and regulatory-compliant, production-ready solutions. ## Work Experience ### AI/ML Engineer @ HSBC Jan 2025 – Present | United States • Worked on AI-driven fraud detection systems for HSBC corporate and card transactions, processing millions daily, reducing fraud exposure by 35%, accelerating risk response by 40%, ensuring SOX and SOC2 compliance, and enabling agentic AI interventions. • Built robust end-to-end data pipelines using Python, SQLAlchemy, and Pydantic, storing structured data in PostgreSQL with 99.5% schema validation accuracy, improving audit readiness by 30%, and supporting real-time monitoring through AWS Bedrock integration. • Engineered transaction-level NLP features, including risk triggers, anomaly descriptors, and temporal sequences using spaCy and rule-based matchers, improving feature quality by 25%, enhancing fraud classification precision by 18%, and integrating agentic AI feedback loops. • Fine-tuned Longformer and FinBERT models on 200K+ labeled HSBC transactions, improving F1 scores from 72% to 88%, reducing compute costs by 40% with LoRA and PEFT, maintaining strong security and compliance controls. • Designed real-time Retrieval-Augmented Generation (RAG) pipelines using LangChain, FAISS, AWS Lambda, and dashboards, increasing historical query accuracy by 30%, reducing investigation time by 35%, and providing fully auditable retraining workflows. • Leveraged AWS Bedrock and agentic AI for automated anomaly detection, real-time transaction risk scoring, and dynamic decision-making, enabling HSBC to proactively mitigate fraud, optimize operational efficiency, and maintain enterprise-grade compliance. ### AI/ML Engineer @ State Street India Pvt.Ltd Jan 2020 – Jan 2023 | Hyderabad, Telangana, India • Developed a financial analytics platform analyzing market data, trade settlements, and portfolio positions, generating risk scores and investment recommendations, reducing decision-making latency by 20% and enhancing operational efficiency across multi-asset global portfolios. • Built scalable batch and streaming pipelines using Azure Data Factory, Databricks, and PySpark, processing structured and unstructured data from Snowflake and PostgreSQL, enabling real-time dashboards, regulatory reporting, and actionable insights for portfolio managers and analysts. • Applied NLP and transformer models to trade notes, research reports, and earnings call transcripts, predicting market risks, trade anomalies, and compliance issues, increasing prediction accuracy by 17% and supporting proactive risk mitigation strategies across teams. • Developed predictive models using gradient boosting, LSTM time-series, and contextual embeddings for asset performance and market trends, deploying via Azure ML and FastAPI microservices in collaboration with DevOps and cloud engineering teams. • Optimized model performance with Optuna hyperparameter tuning and SHAP interpretability, achieving RMSE 5.9, Precision@3 80%, and AUC 0.89, outperforming legacy financial scoring models while ensuring regulatory compliance and audit-ready transparency. • Implemented real-time trade monitoring, alerting, and exception handling services using Kafka, Docker, and Kubernetes, working closely with DevOps and platform teams to maintain 99.7% uptime and streamline operational risk management. • Automated model retraining, validation, and drift detection workflows using Azure ML pipelines, MLflow, Prometheus, and Grafana dashboards, monitoring predictive performance and operational KPIs, achieving 93% SLA compliance and data-driven investment outcomes. ## Education ### Master of Science - MS in Computer Science Ohio University ### Bachelor of Technology - BTech in Computer Science Jawaharlal Nehru Technological University Hyderabad (JNTUH) ## Contact & Social - LinkedIn: https://linkedin.com/in/tejaswi-jonnadula --- Source: https://flows.cv/tejaswij JSON Resume: https://flows.cv/tejaswij/resume.json Last updated: 2026-04-16