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Vetted Mockito Professionals
Pre-screened and vetted.
Senior Software Engineer specializing in distributed systems and FinTech
Mid-Level Software Development Engineer specializing in AWS serverless, security, and ML platforms
Senior Full-Stack Software Engineer specializing in banking platforms on AWS
Mid-level Full-Stack Developer specializing in cloud-native microservices and commerce platforms
Mid-Level Software Engineer specializing in FinTech, treasury systems, and real-time data pipelines
Staff-level Backend Engineer specializing in distributed data platforms and AI infrastructure
Senior Software Engineer specializing in cloud-native distributed systems
Mid-Level Full-Stack Java Developer specializing in Spring Boot and Angular/React
Mid-level Full-Stack Developer specializing in AI and FinTech platforms
Mid-level Full-Stack Developer specializing in cloud-native apps, AI/ML, and microservices
Junior Software Engineer specializing in backend systems and cloud messaging
Junior Full-Stack Software Engineer specializing in React, Node.js, and cloud deployments
Mid-level Full-Stack Engineer specializing in cloud-native data and enterprise platforms
Senior Full-Stack Software Engineer specializing in scalable microservices and cloud platforms
Entry-Level Software Engineer specializing in backend systems and cloud messaging
Pavan Devulapalle
Mid-level Software Engineer specializing in cloud platforms and AI-integrated full-stack development
“Backend engineer who built Flask-based internal APIs supporting GenAI-driven provisioning/diagnostics (Outpost/AWS Outposts-like environment), with deep hands-on optimization across Postgres/SQLAlchemy (2s to <200ms endpoint improvement). Experienced integrating ML/LLM workflows via AWS SageMaker and Bedrock, and designing multi-tenant isolation plus high-throughput Redis-backed background task pipelines (minutes to seconds).”
Poorna Pedapudi
Mid-Level Software Engineer specializing in distributed backend systems and cloud-native microservices
“Software engineer focused on data platforms and applied LLM systems: built an internal data quality monitoring layer to catch silent data drift and iterated post-launch after finding ~30% false-positive alerts, reducing noise via dynamic baselines and improved structured logging. Also shipped a production RAG-based internal knowledge assistant over Jira/Confluence with citations, confidence-based fallbacks, and nightly automated evals to prevent regressions.”