.png){kind=logo}
Vetted Flask Professionals
Pre-screened and vetted.
Soham Ravindra Lokhande
Junior Software Engineer specializing in agentic automation and AI platforms
“Backend-leaning founding/early engineer who built automation platforms end-to-end: FastAPI/Python services integrated with a Next.js/TypeScript frontend, including a production VNC streaming URL endpoint for cloud-instance desktop viewing. Also designed core Postgres user/workflow data models and built an agentic orchestration system with LangChain/LangGraph (sub-agents, validators, pause/resume), plus made scalability tradeoffs like S3 pre-signed uploads to keep microservices responsive.”
Ranxin Li
Mid-Level AI/Full-Stack Engineer specializing in agentic LLM systems and RAG
“Built and deployed Clyra.AI, an AI-driven daily scheduling product that uses a LangGraph-based multi-agent LLM pipeline (task extraction, verification, reflection) grounded with strict RAG over emails/documents/calendars and real-world signals like health metrics. Designed a custom agent orchestrator with bounded loops/termination conditions and a self-auditing verification/reflection layer to reduce hallucinations while controlling latency and cost via caching and model distillation.”
Srichandan Kota
Senior Full-Stack AI Engineer specializing in Generative AI and FinTech
“Backend engineer who built and owned an AI-powered financial research product end-to-end, using a typed NestJS/GraphQL backend with LangGraph-style agent routing to produce sourced, structured financial analysis. Emphasizes finance-grade correctness (Zod validation, metric registries, unit/empty-result guardrails) while keeping latency low via batching, caching, and fast token streaming, and has led incremental migrations using strangler/feature-flag/shadow traffic patterns.”
Veena Vyshnavi Garre
Senior Full-Stack Software Engineer specializing in cloud-native systems and AI/ML
“Backend engineer who significantly evolved an internal Resource Manager platform, moving from a monolith to microservices and improving onboarding speed while reducing integration errors. Has hands-on experience building reliable and secure Python/FastAPI APIs (Pydantic schemas, circuit breakers, caching, metrics/alerts) and leading zero-downtime migrations with strong data integrity patterns (dual writes, idempotency, reconciliation checks).”
Atharva Deshmukh
Mid-level AI/ML Engineer specializing in GenAI and cloud MLOps
“Applied LLMs to high-stakes domains (wildfire risk for emergency teams and loan approval via a fine-tuned IBM Granite model), with a strong focus on reliability—using RAG-based cross-validation to reduce hallucinations and continuous ingestion pipelines (MODIS satellite imagery via AWS Lambda) to keep data current. Experienced in production orchestration and MLOps-style workflows using Airflow, AWS Step Functions, and SageMaker Pipelines, and collaborates closely with analysts on KPI-driven evaluation.”
PremKumar Gandla
Mid-level AI/ML Engineer specializing in MLOps, NLP, and scalable model deployment
“Built and deployed a production autonomous AI data analyst agent (LangChain + GPT + Streamlit on AWS) that turns natural-language questions into validated SQL, visualizations, and insights, cutting manual analysis time by ~50%. Emphasizes reliability and MLOps: schema-aware validation/guardrails to prevent hallucinations, scalable large-data processing, and Azure DevOps CI/CD + MLflow for automated deployment and experiment tracking.”
George Stathopoulos
Entry-level Software Engineer specializing in full-stack web development and applied systems work
“Full-stack developer with hands-on experience building an end-to-end automated trading platform that combines web scraping, relational data storage, Flask/React architecture, and LLM-based decisioning via Google Vertex AI. Also brings production experience at CALEC, where they contributed frontend improvements including welcome-page redesigns, multilingual support, and accessibility-related fixes.”
SREYAS GANGJI
Mid-level Software Engineer specializing in AI/ML backend systems
“AI/data engineer at ZS Associates focused on production-grade agentic systems, FastAPI microservices, and cloud-native ETL/RAG pipelines at significant scale. They’ve built multi-agent validation and diagnostic workflows inspired by their Copilot/KUBEPILOT AI work, supporting 500K+ records per day while improving ML inference performance by ~30% and cutting manual troubleshooting by 60%.”
Raj Kale
Junior Software Engineer specializing in full-stack systems and FinTech
“Full-stack engineer with experience building financial and hiring-product systems, spanning React/TypeScript dashboards, Flask/Kafka/Postgres backends, and multi-tenant configuration for 3,000+ clients. Stands out for combining deep technical debugging and performance work with product-minded UX improvements, including a 41% lift in resume matching accuracy and ~40% latency reduction through batching and query tuning.”
Jay Patil
Mid-level Full-Stack AI/ML Engineer specializing in LLMs and intelligent systems
“Built a semantic search portal for a fellowship department and an AI-driven PR review pipeline, using AI selectively for boilerplate while retaining full ownership of architecture, security review, testing, and deployment. Has hands-on experience with multi-agent systems, monitoring, and security validation, with a notably disciplined approach to catching false positives and rewriting weak AI output.”
Ankur k
Entry-level Smart Contract Engineer specializing in DeFi and blockchain systems
“Full-stack product builder with experience owning a B2B bulk-purchase negotiation feature end to end and layering AI into real workflows like invoice generation. Also built an agentic deployment tool using LangGraph, MCP, OCR, and Playwright to automate app deployment to platforms like Vercel and Render.”
Nidhip Patel
Mid-level Software Engineer specializing in AI/ML and full-stack development
“Backend Java engineer with strong platform/DevOps experience: modernized an insurance claims legacy monolith into DDD-aligned microservices, deployed containerized services on Kubernetes with Jenkins CI/CD and static analysis gates, and implemented GitOps using ArgoCD. Also led major AWS migration planning with dependency mapping and network monitoring to uncover hidden dependencies, and built Kafka-based real-time event streaming with schema-registry-driven evolution.”
Julia Vavrinyuk
Senior Software Engineer specializing in cloud, GenAI, and SaaS solutions
“Candidate combines B2B SaaS sales experience with hands-on technical delivery, spanning full-cycle selling at Consensus Cloud Solutions and GenAI POC leadership at Capgemini. Particularly interesting for Solutions Engineering roles that need someone comfortable with enterprise demos, AWS-based AI architectures, regulated-industry customers, and translating technical trade-offs into business decisions.”
Sai Revanth Evani
Mid-level AI/ML Engineer specializing in RAG systems and Python cloud backends
“Frontend engineer with hands-on experience building AI-powered document search and analytics products, including RAG-based knowledge retrieval interfaces with citations, filters, and document previews. Stands out for combining React/TypeScript architecture with production performance tuning using profiling tools, memoization, lazy loading, and debounced data flows to keep complex, document-heavy UIs responsive.”
SAM AUSTIN
Senior Full-Stack Engineer specializing in Python, cloud, and SaaS platforms
“Lead full-stack engineer with strong Python backend depth and hands-on React/TypeScript experience, working in startup-like B2B SaaS environments serving enterprise software customers such as CrowdStrike, HashiCorp, and VMware. Stands out for redesigning tightly coupled systems into modular AWS-based microservices and building configurable integration platforms that improved enterprise customer onboarding and marketplace workflow scalability.”
Dinesh Guguloth
Mid-level Software Engineer specializing in full-stack cloud-native applications
“Full-stack engineer with cloud and GenAI experience who has owned production features end-to-end, including a reporting dashboard optimized from 14s to 5s using query/API refactoring and monitored via AWS CloudWatch. Also productionized an OpenAI-powered chatbot using LangChain with prompt design, guardrails, and evaluation via production logs and user feedback, and has led incremental legacy-to-microservices modernization with parallel run to avoid regressions.”
Aishwarya Murahari
Senior Full-Stack Engineer specializing in cloud-native and AI-powered systems
“Full-stack engineer with experience spanning React/TypeScript frontends, Node.js backend services, and production systems on AWS. They’ve built real-time, event-driven inventory workflows using PostgreSQL, Redis, RabbitMQ, and Keycloak, and also drove a multi-agent LangGraph architecture for a self-healing systems project evaluated on simulated Kubernetes outages.”
Aditya Anil Raut
Junior Software Engineer specializing in AI/ML, data pipelines, and cloud APIs
“Hands-on AI/LLM practitioner who built a RAG-based customer support chatbot and tackled production issues like data chunking complexity and response-time lag. Uses techniques such as overlapping chunks, semantic search, context engineering, and query routing, and has experience presenting technical demos/workshops to developer audiences.”
Abdul Tanimu
Senior Full-Stack Software Engineer specializing in cloud-native web applications
“Backend/data engineer who built a production booking platform on FastAPI microservices (Postgres/Redis/gRPC) and delivered AWS infrastructure spanning Lambda, ECS, SQS, and Glue-to-Redshift analytics. Demonstrated measurable SQL optimization (10 minutes to <40 seconds) and strong operational ownership through monitoring, incident response, and schema-evolution hardening.”
Vaidik Vyas
Mid-Level AI Backend Engineer specializing in Python, LLM/RAG, and healthcare/insurance platforms
“AI Backend Engineer in MetLife’s claims technology group who built and deployed a production LLM-based decision support system that helps claim adjusters quickly find relevant policy rules from long PDFs and historical notes. Designed it as multiple production-grade services with retrieval-first guardrails, continuous validation, and Airflow-orchestrated pipelines for ingestion, embeddings, and vector index updates to keep the system reliable as policies and data evolve.”
Aryaa Deshpande
Junior AI Engineer specializing in ML, LLM systems, and RAG
“Built and deployed an LLM/applied-ML system enabling efficient extraction of useful information from large unstructured multimodal datasets, owning the full pipeline from ingestion to inference and APIs with a strong emphasis on production reliability, latency, and monitoring. Also delivered a voice-based AI workflow for Hindi policy document access for the Election Commission of India by translating non-technical usability needs into iterative demos and a successful implementation.”
Sudeepti Chalamalasetti
Mid-level AI/ML Engineer specializing in LLMs, RAG, and MLOps
“Built a production RAG-based healthcare chatbot to retrieve patient medical documents spread across multiple platforms, reducing manual and error-prone searching. Implemented semantic search with custom embeddings (Hugging Face) and Pinecone, deployed via FastAPI/Docker on AWS SageMaker with MLflow tracking, and optimized fine-tuning cost using LoRA while orchestrating retraining pipelines in Airflow.”
Karthik Gantasala
Mid-level Generative AI Engineer specializing in LLM agents and RAG
“GenAI/LLM engineer who built and deployed a production RAG system for enterprise document search and decision support, cutting manual lookup time by 40%+. Experienced with LangChain/LangGraph agent orchestration plus Airflow/Prefect for ingestion and incremental reindexing, with a strong focus on reliability (testing, observability) and stakeholder-driven metrics.”
Pranav Marla
Mid-level AI/ML & Full-Stack Engineer specializing in LLM agents and generative AI
“LLM/agent builder who shipped a live consumer AI-agent app (kalpa.chat) that visualizes complex reasoning as interactive graphs and abstracts multi-provider model usage via a unified wallet. Professionally has applied LangChain/LangGraph to IVR parsing and to scaling a football video-generation pipeline at DAZN, including shipping a VAR-specific retrieval/order fix via SQL after iterating with a non-technical PM.”