• Engineered multi-step agentic workflow systems integrating LLM-driven decisioning and API execution, improving task completion rate by 35% and reducing manual merchant operations across platform workflows

• Built end-to-end orchestration pipelines with structured prompt-to-API execution, reducing workflow latency by 25% and increasing automation reliability through optimized state management and retry handling

• Developed evaluation frameworks combining LLM-as-judge and human feedback loops, accelerating model iteration cycles by 40% while improving response accuracy and reducing hallucination rates in production systems

• Designed scalable backend services using Python, FastAPI, and microservices architecture, implementing REST and GraphQL APIs with focus on idempotency, fault tolerance, and distributed system reliability

• Implemented agent orchestration systems using state machines, asynchronous processing, and event-driven architecture, enabling reliable multi-step workflow execution with retry logic, branching, and monitoring

• Applied advanced prompt engineering, tool-calling techniques, and context management strategies to enhance LLM decision-making accuracy, ensuring safe and controlled execution of automated business workflows

• Built evaluation and experimentation pipelines with dataset versioning, experiment tracking, and performance metrics ncluding latency, task success rate, and consistency for continuous system improvement

• Developed cloud-native services on AWS using containerization, Kubernetes, and CI/CD pipelines, enabling scalable deployment, automated testing, and reliable production releases for distributed AI systems

• Implemented observability and monitoring using AWS CloudWatch, distributed tracing, and logging frameworks,

improving system visibility, debugging efficiency, and proactive issue resolution in production environments

• Engineered end-to-end AI-driven data platform integrating real-time pipelines and ML inference APIs, improving prediction accuracy by 35% and reducing decision latency by 40% across enterprise workflows

• Developed scalable microservices architecture for ML model deployment, increasing system throughput by 50% while reducing API response time to sub-200ms through optimized request handling and caching strategies

• Built integrated analytics dashboards and automated pipelines, improving business visibility by 45% and enabling datadriven decisions that reduced operational costs by 30% across multiple client use cases.

• Designed and implemented RESTful APIs using Python and FastAPI, ensuring high availability, fault tolerance, and idempotent service design aligned with distributed microservices architecture principles

• Developed dynamic frontend interfaces using React and TypeScript, enabling seamless user interaction, real-time data visualization, and responsive UI/UX design across complex analytics-driven enterprise applications

• Built and optimized data pipelines using PySpark and Kafka, enabling efficient batch and real-time processing with strong data validation, schema enforcement, and scalable event-driven architecture patterns

• Collaborated cross-functionally with data scientists, product managers, and stakeholders, applying agile methodologies, communication, and problem-solving skills to deliver high-impact enterprise AI-driven solutions

• Deployed containerized applications using Docker and Kubernetes on AWS, enabling scalable cloud-native infrastructure with automated CI/CD pipelines, improving deployment efficiency and system resilience in production environments

• Designed cloud-based data architectures on AWS using services like S3, Lambda, and API Gateway, ensuring secure, scalable, and high-performance data processing and API management across distributed systems