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Vetted Flask Professionals
Pre-screened and vetted.
Junior Software Engineer specializing in backend systems and cloud messaging
Executive Engineering Leader specializing in AI automation and cloud platforms
Mid-level Python Backend Developer specializing in FinTech and ML-driven fraud detection
Senior Full-Stack Engineer specializing in backend systems and cloud-native microservices
Mid-level Full-Stack Engineer specializing in Python microservices and cloud automation
Senior Data/GenAI Engineer specializing in cloud-native ML, RAG, and real-time data platforms
Mid-Level Frontend Software Engineer specializing in React and React Native
Mid-level Full-Stack Software Engineer specializing in AWS microservices and web platforms
Mid-Level Full-Stack Python Engineer specializing in AI-powered web apps and cloud-native systems
Junior Robotics & AI Engineer specializing in autonomous systems and machine learning
Junior AI/ML Engineer specializing in agentic AI and cloud optimization
Mid-level Software Development Engineer specializing in backend systems and ML platforms
Senior Full-Stack Engineer specializing in AI automation and LLM-powered products
Mid-level AI/ML Engineer specializing in recommendation, retrieval, and MLOps
Mid-level Full-Stack Developer specializing in AWS modernization and Java/Angular
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).”
Vignesh Shanmugasundaram
Junior Software Engineer specializing in full-stack development and applied ML
“Full-stack engineer with experience at Zoho and Amazon who has owned production systems end-to-end, including a monolith-to-microservices migration using Kafka and Cassandra that improved search latency ~25% and increased throughput without data loss. Also built a hackathon project (Buildwise) into a sold product for a construction company (AI-driven document compliance checks) and shipped an IoT-based parking availability MVP in 3 weeks.”
Nehal Mahankali
Mid-level Full-Stack Python Developer specializing in cloud-native banking applications
“Backend engineer who built a low-latency real-time transaction API in Python/Flask, with strong depth in PostgreSQL/SQLAlchemy performance tuning (time-based partitioning, indexing, connection pooling). Has production experience integrating ML scoring and OpenAI-style APIs with safety/latency controls, and designing multi-tenant isolation strategies including per-tenant pooling/caching and premium-tenant isolation.”
Shuju Sun
Mid-Level Software Engineer specializing in real-time data pipelines and ML deployment
“Ticketmaster data engineer who built CDC-driven Kafka pipelines feeding Snowflake for analytics and data science teams. Hands-on in production operations—scaled Kafka during sudden playoff-driven transaction spikes and improved monitoring for preemptive scaling. Known for using small-batch experiments and quantitative metrics to align stakeholders and drive cost-saving architecture changes (e.g., buffering to reduce AWS Lambda invocation frequency).”
winston lo
Junior Software Engineer specializing in AI agents, RAG, and full-stack development
“Backend engineer who built and iterated a secure, multi-tenant RAG system over a large document corpus, emphasizing strict RBAC/ACL isolation, hybrid retrieval (vector+keyword), reranking, and strong observability to balance relevance, latency, and cost. Also led production refactors/migrations using strangler + feature flags/dual writes and has experience catching subtle real-world failure modes (including in a sensor calibration optimization pipeline).”