Vetted Flask Professionals

Yishan Hsueh

Screened

Junior Full-Stack Engineer specializing in scalable web platforms and FinTech

San Jose, CA2y exp

“Full-stack product engineer with end-to-end ownership across React, TypeScript, backend APIs, and AI-assisted workflows. Built a scheduling management feature at Intuit that enforced complex business rules and reportedly reduced costs by about 25%, and also created a secure monorepo platform for their own jewelry business with access control and RLS from the start.”

“At KPMG, built and productionized a secure RAG-based LLM assistant that lets business and risk stakeholders query data warehouses in natural language, reducing dependence on data engineers for ad-hoc analysis. Demonstrates strong production rigor (Airflow orchestration, CI/CD, containerization), retrieval/embedding tuning (rechunking, semantic abstraction for structured data), and reliability controls (confidence thresholds, refusal behavior, monitoring and canary evals).”

“Open-source JavaScript contributor focused on performance and maintainability in data visualization libraries—refactored legacy ES5 into modular ES6, added tests/docs, and delivered ~30% faster load times with positive community adoption. Also optimized a React dashboard (~40% load-time reduction) and took ownership in an ambiguous AI product initiative by setting milestones, standing up an initial ML pipeline, and shipping a prototype in ~6 weeks that became the basis for production.”

“Software engineer who owned end-to-end delivery of customer-facing agricultural forecast reporting (crop yield/health) and iterated quickly via rigorous edge-case testing and customer feedback. Also built an internal ML training platform (TypeScript/React + Flask/Python + MongoDB) used by every developer, with architecture designed to stay responsive under heavy compute load.”

“Developer with experience across Drupal and Java/Spring Boot applications using React/jQuery for UI and API-driven features. Has handled production issues by tuning reverse proxy timeouts for login problems and troubleshooting data pipeline inaccuracies by fixing database queries, with a focus on performance and careful verification before changes.”

“ML/LLM practitioner who has shipped production RAG systems (summarization + Q&A) and end-to-end Airflow-orchestrated demand forecasting pipelines at NEON IT. Strong focus on reliability—uses evaluation scripts, retrieval/chunking tuning, validation/retries/alerts, and stakeholder-driven iteration to make AI workflows consistent and usable.”

“Backend engineer who built Flask-based microservices for a high-throughput risk engine, using Kafka for streaming decoupling and Redis for low-latency caching, with PostgreSQL + Cassandra for mixed relational and time-series needs. Has hands-on experience productionizing ML inference (Azure OpenAI/TensorFlow) behind REST APIs with async queues, batching, and caching, plus multi-tenant isolation via schema separation and RBAC with per-tenant rate limiting.”

“Worked in a startup building an automated carbon accounting/climate reporting product, partnering with client IT and internal cross-functional teams to ship features and train end users. Also has software engineering internship experience debugging complex multi-workflow systems, including uncovering a significant (~20%) data annotation error by instrumenting and testing each workflow step.”

“Backend/ML engineer working on a battery energy storage system operations dashboard: built a Flask backend integrated with OAuth and a separate FastAPI optimization/simulation service, deployed via Docker CI/CD to Azure Container Apps. Strong in productionizing ML (AzureML to batch endpoints) and in performance/scalability patterns (Postgres indexing/JSONB, per-unit data isolation, async throttling + caching for year-long CPU-intensive simulations across 40+ scenarios).”

“ML/GenAI engineer with recent CVS Health experience building a production RAG system over unstructured financial/research documents using LangChain, FAISS, and Pinecone, plus LoRA/PEFT fine-tuning of GPT/LLaMA for domain-aware summarization. Demonstrates strong applied MLOps and data engineering skills (Airflow/Prefect, Docker/Kubernetes, CI/CD, MLflow) and measurable impact (sub-second retrieval, ~40% better context retrieval, ~25% entity matching improvement).”

“Backend-leaning product engineer with DevSecOps depth who has shipped real-time, Kafka-driven data pipelines and AI-enabled customer-facing features to production on AWS. Built a Spring Boot API layer serving real-time predictions at 100K+ requests/day, improving latency by 35% and user task completion by ~25%, and delivered a React/TypeScript dashboard plus a Postgres audit/history model optimized for search and large event volumes.”

“Full-stack engineer who shipped a group scheduling SaaS feature with live availability updates using Next.js App Router + TypeScript, owning production reliability after launch (auth debugging, monitoring, polling/backoff tuning). Has hands-on experience with Postgres schema/index design and query optimization (EXPLAIN ANALYZE) and building durable orchestrated backend workflows with retries and idempotency.”

“GenAI/LLM ML engineer (currently at Webprobo) building an enterprise GenAI platform with document intelligence and automation on AWS and blockchain. Has hands-on experience with RAG, LLM evaluation tooling, and orchestrating production LLM workflows with Apache Airflow, plus deep exposure to reliability challenges in globally distributed/edge deployments. Also partnered with business/marketing stakeholders at a banking client to deliver an AI-driven customer retention insights solution.”

“Data engineer focused on reliable, scalable analytics pipelines and external data collection. Has owned end-to-end pipelines processing 5–10M records/day, serving Snowflake data marts to Power BI/Tableau, and reports ~99% reliability through strong validation/monitoring. Also shipped versioned REST APIs for curated data with query optimization and caching.”

“Built an NYU software engineering capstone called “Smart Cash AI,” a multi-agent LLM-powered web app that curates offline-ready podcasts/articles/videos/news based on user preferences and commute schedules. Architected agent orchestration (discovery/downloader/summarizer), real-time progress via WebSockets, and an ETL normalization layer across RSS/YouTube and other sources with GUID-based deduplication, retries, and failure isolation to keep the system predictable.”

Rishitha reddy katamareddy

Screened

Mid-level Generative AI & Machine Learning Engineer specializing in agentic LLM systems

USA4y exp

“Built and deployed a production agentic LLM knowledge assistant that answers complex questions over internal documents, APIs, and databases using a RAG architecture (FAISS/Pinecone) and LangChain/LangGraph orchestration. Emphasizes production-grade reliability and hallucination control through grounding, confidence thresholds, validation, retries/fallbacks, and full observability (logging/metrics/traces) with continuous evaluation and feedback loops.”

Hamidreza Lotfalizadeh

Screened

Mid-level AI/ML Engineer specializing in LLM agents, RAG, and ML systems

Bay Area, CA6y exp

“At Inertia Systems, built a production LLM-powered ingestion pipeline that converts heterogeneous sources (PDF/JSON/IFC/SQL and financial tables) into standardized text and uses GraphRAG to construct a knowledge graph with verified dependency relationships. Also has hands-on HPC orchestration experience with SLURM, including creating a custom wrapper process manager to improve resource utilization under restrictive scheduling policies.”

Sumit Kothari

Screened

Junior AI/Full-Stack Engineer specializing in LLM apps and RAG systems

Los Angeles, CA1y exp

“AI engineer who built and shipped a production AI document-understanding/search system at Sumeru Inc, including a full RAG + LLMOps evaluation stack (MLflow, DeepEval, RAGAS) deployed on GCP. Also developed LangChain/LangGraph multi-agent workflows for UAV flight-log analysis and has experience presenting AI solutions to non-technical stakeholders and prospect clients to drive POCs.”

Daniel Jin

Screened

Intern Site Reliability Engineer specializing in Kubernetes, AWS, and observability

New York, NY1y exp

“Backend/data engineering candidate specializing in Python/Flask services and ML-enabled systems, deploying containerized workloads on AWS ECS/EKS with strong observability (Prometheus/Grafana) and PostgreSQL performance tuning. Built multi-tenant architectures with row- and schema-level isolation and optimized a Kubernetes-based Airflow + Spark nightly ETL pipeline for an e-commerce client, improving performance by 250%+ and reliably beating morning reporting deadlines; also contributed to Apache Airflow (SQLAlchemy/PostgreSQL area).”

Sai Nekkanti

Screened

Mid-level Data Scientist / ML Engineer specializing in secure GenAI and financial compliance

Mount Laurel, NJ4y exp

“Built a production "sentinel insight engine" to tame information overload from millions of product reviews and support transcripts, combining Azure OpenAI (GPT-3.5) zero-shot classification with a fine-tuned T5 summarizer to generate weekly actionable product insights. Demonstrated strong MLOps/production engineering by adding drift monitoring with embedding-based detection, integrating REST with legacy SOAP/queue-based CRM via FastAPI middleware, and scaling reliably on Kubernetes with HPA.”

Nishad Kane

Screened

Mid-level Data Scientist & AI Engineer specializing in RAG, agentic AI, and production ML

5y exp

“AI/data engineer who built a production LLM-powered schema drift detection system (LangChain/LangGraph) to catch semantic data changes before they break downstream analytics/ML. Deployed on AWS with Docker/S3 and implemented an LLM-as-a-judge evaluation framework to improve trust, reduce hallucinations, and control false positives/alert fatigue. Collaborated with non-technical risk/business analytics stakeholders at EY by delivering human-readable drift explanations that improved confidence in financial analytics dashboards.”

Sreelekha Vuppala

Screened

Mid-level Data Scientist specializing in Generative AI, MLOps, and cloud data platforms

USA4y exp

“GenAI/ML engineer (CitiusTech) who has deployed production RAG systems for compliance/operations document Q&A, using Pinecone + FastAPI microservices on Kubernetes with strong monitoring and guardrails. Also built a GenAI-powered incident triage/routing solution in collaboration with non-technical stakeholders, achieving 35% faster response times and 40% fewer misclassified tickets, and has hands-on orchestration experience with Airflow and AutoSys.”

Harini Vinu

Screened

Intern Software Engineer specializing in cloud, big data, and test automation

New York, United States1y exp

“Internship experience at Qualitest building and deploying an LLM-powered test automation system that reduced manual test creation and improved efficiency (~40%). Demonstrates strong production engineering for LLM systems (timeouts/retries/monitoring/caching, prompt optimization, batching) and has scaled workflows to 100+ concurrent jobs; also has orchestration experience with AWS Step Functions and Kubernetes.”