Pavan Kalyan
Al Ml Engineer @State Street
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WORK HISTORY
Al Ml Engineer @State Street
US
Engineered scalable machine learning pipelines using Python, PySpark, and AWS Glue to process and analyze multi-terabyte financial datasets, enabling real-time portfolio risk predictions while maintaining compliance with strict regulatory frameworks.Designed and optimized deep learning models leveraging TensorFlow, PyTorch, and LSTM architectures to forecast asset trends and identify early warning signals, enhancing portfolio stability and improving risk assessment accuracy by 25%.Developed and deployed reinforcement learning-based portfolio optimization models via AWS SageMaker and integrated them with Apache Kafka streaming, which accelerated predictive decision-making speed by 40% for traders and portfolio managers.Implemented unsupervised anomaly detection frameworks using Isolation Forest and Autoencoders to identify suspicious trading activities proactively, enabling compliance teams to investigate irregularities faster and reduce monitoring overhead by 30%.Designed and delivered interactive dashboards using Tableau, Power BI, and Snowflake to visualize real-time portfolio risks, automate stress-testing scenarios, and support portfolio managers in making data-driven investment strategies.
EDUCATION
University of North Texas
Master's degree, Computer Science
Hindustan University
Bachelor of Technology - BTech, Mechanical Engineering
ABOUT PAVAN KALYAN
I’m an AI Engineer with a strong background in Software and Data Engineering, building applications that bring together Generative AI, Natural Language Processing (NLP), and scalable backend systems. Starting as a Programmer Analyst in Data and AI, I worked with cloud platforms, Python, and MLOps to design pipelines and deploy machine learning models. That early foundation shaped how I now build AI-powered applications that are secure, auditable, and ready for production.What I bring to the table is the ability to merge full-stack engineering with AI innovation. I’ve worked with LLMs like GPT-4, Claude, LLaMA, and Hugging Face Transformers, and frameworks such as LangChain, Pinecone, and Supabase to create retrieval pipelines, prompt workflows, and intelligent assistants. On the engineering side, I build APIs with FastAPI/Node.js, deploy with Docker and Kubernetes, automate infra using Terraform, and add observability with OpenTelemetry.What I can do includes:AI Software Engineering: APIs, scalable microservices, React-based operator tools.AI/ML Development: NLP pipelines, classification, fine-tuning with TensorFlow, PyTorch, Hugging Face.Generative AI: Prompt design, LangChain workflows, RAG with Pinecone/Supabase, LLM agents.Full-Stack AI Systems: LLM-powered apps with frontend + backend + databases (PostgreSQL, MongoDB).MLOps & Deployment: Kubernetes, Docker, Terraform, Jenkins, GitHub Actions across AWS, Azure, GCP.Agentic Workflows: Multi-step orchestration with LangChain Agents, AutoGen, LangGraph.By combining a data/ML foundation with AI application development, I can bridge traditional software systems with next-generation AI. I’ve applied this across healthcare, banking, insurance, and e-commerce, where the common challenges are reliability, compliance, and scale. My focus is turning advanced AI into practical, production-ready systems that deliver measurable impact.I’m open to roles including:AI Software EngineerAI/ML EngineerFull Stack AI DeveloperGenerative AI EngineerAI Application EngineerSoftware Engineer (AI-Focused)Let’s connect if you’re building at the intersection of software, data, and AI and need someone who can turn models, pipelines, and ideas into scalable applications.
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