Vatsal P.

About Vatsal P.

Currently open to Senior Backend Engineer, Python Backend Engineer, and AI Platform Engineer roles.Senior Backend Engineer with 5+ years of experience building scalable Python systems, distributed data platforms, and cloud-native backend services.I specialize in designing high-performance APIs, event-driven architectures, and production-ready data pipelines used in regulated environments including fintech, defense, and enterprise platforms.Core expertise includes:• Python backend development (FastAPI, Django, Flask)• Microservices & distributed systems• Data platforms and ETL pipelines (Airflow, Spark, Kafka)• Cloud-native infrastructure (AWS, GCP)• High-performance APIs and backend architecture• PostgreSQL, DynamoDB, Redis optimization• Observability and production reliability• GenAI backend systems (RAG pipelines, vector search, LLM APIs)Recent work includes building production-grade backend services, distributed data pipelines, and AI-enabled platforms integrating LLM workflows with modern cloud infrastructure.I enjoy solving complex backend problems, improving system scalability, and building reliable systems that power modern applications.Open to:Senior Backend EngineerPython EngineerAI Platform EngineerDistributed Systems Engineer

Experience

1. Full Stack Python Developer

   Bayside Solutions

   Mar 2026 — Present · US

   Design and develop AI-driven backend systems for structured data processing, focusing on intelligent tagging and semantic enrichment of large-scale datasets.• Build and maintain a multi-strategy tagging agent that classifies database columns using: • LLM-based inference (Gemini, Claude, internal models) • Regex-driven rule engines • Metadata-based classification pipelines• Architect scalable Python-based services to orchestrate parallel tagging workflows, optimizing throughput and latency across large data volumes.• Implement prompt engineering and evaluation frameworks to improve model performance, including accuracy, precision, and recall of tagging outputs.• Develop robust data processing pipelines integrating structured metadata (column names, descriptions) with AI models for automated classification.• Design modular backend systems using clean architecture principles, ensuring extensibility across multiple tagging strategies and model integrations.• Optimize system performance through asynchronous processing, batching strategies, and efficient API design for high-throughput workloads.• Collaborate in a distributed, remote environment using version control, code reviews, and structured development workflows to deliver production-ready systems.• Contribute to the integration of generative AI capabilities into enterprise data platforms, improving automation and reducing manual data labeling effort.

Education

• San Francisco Bay University

  Master of Science - MS, Computer Science