Vetted AWS Lambda Professionals

“Data engineer in financial services (Northern Trust) who has worked across ingestion, transformation, data quality, orchestration, and serving on AWS (S3/Glue/EMR) with Airflow. Highlights include processing ~15M transactions with validation/anomaly detection for regulatory reporting and improving Snowflake query performance by 27% for risk/compliance reporting. Also built a personal real-time streaming service (FastAPI, Kafka, Redis, Cassandra) and uses production reliability patterns like blue-green/atomic swaps and robust retry strategies.”

Punith Jayaramu

Screened

Mid-Level Full-Stack Software Engineer specializing in FinTech and Mortgage systems

Auburn Hills, MI3y exp

“Full-stack engineer with deep AWS serverless and reliability experience in fintech/underwriting systems, including eligibility scoring and dynamic rule deployments. Built and productionized an LLM-powered incident RCA assistant (Bedrock Claude 3 + custom RAG + React) achieving 92% precision and ~75% MTTR reduction, with mature guardrails (evals, drift monitoring, HITL, audit logs) and strong operational rigor (canaries, chaos testing, DLQ remediation).”

Sree Gautham Alluri

Screened

Mid-level Robotics & AI Developer specializing in autonomous navigation and LLM-powered robotic systems

Exton, PA6y exp

“Robotics Support Engineer at HAI Robotics supporting a 385-robot warehouse fleet at a Shein client site. Built a production automation and reporting workflow to diagnose and resolve abnormal shelf locations, cutting incidents from ~250/day to ~25/day while providing actionable root-cause data to client/ops/maintenance. Hands-on ROS 2 (Humble) debugging across Nav2/localization/TF and sensor integration issues including QoS and firmware coordination.”

Lohith Burra

Screened

Intern Technical Artist specializing in Unity real-time 3D visualization

Remote2y exp

“Unity/VR developer working on real-time Formula 1 broadcast simulations, including a 3D VR experience that lets users follow any car and compare qualifying laps using live F1 data. Built a proprietary math engine and JSON-based pipeline to drive accurate real-time car positioning in Unity; the 7-month effort became the team’s MVP and significantly increased app engagement.”

Sridhar Duggireddy

Screened

Executive Technology Leader specializing in Cloud Platforms, DevOps, and AI/ML

Tampa, FL21y exp

“Early-stage startup CTO helping build an AI-powered parenting assistant app with features spanning advice, shopping, task management, and inventory management. The team is currently testing an MVP with their network while the candidate simultaneously learns the seed/Series fundraising process through Connectd and early investor conversations.”

“Frontend-leaning engineer at Exchange Lodge who recently took over an entire system end-to-end after team attrition, expanding into backend and platform ownership. They modernized a JSON-driven UI platform into a TypeScript/Angular microfrontend architecture, reporting an ~85% productivity gain, and also rebuilt documentation and developer workflows into a monorepo with AI-friendly Markdown and Mermaid diagrams.”

“LLM/agent engineer who has built production multi-agent systems (LangChain/LangGraph) for enterprise workflows like email and calendar automation, with a strong focus on latency, tool-calling accuracy, and evaluation via LangSmith. Also worked on AI long-term memory using knowledge graphs at VEAI and communicated the approach and tradeoffs to CEO/CTO stakeholders.”

“Frontend engineer (Teradata) who led an enterprise analytics dashboard end-to-end, building complex React + TypeScript interfaces with near-real-time updates and large data volumes. Strong track record in scalable frontend architecture, performance tuning (render optimization, payload reduction), and quality at speed via CI/CD, automated regression testing, and incremental rollouts in an Agile environment.”

“Engineering leader who delivered a Magento-to-Shopify rebuild ahead of schedule while maintaining 100% team retention over 4 years at Hydrobuilder Holdings. Also built and ran a concert-listing platform (TourVolume) in production for 12+ years (Webby Honorable Mention) and later open-sourced the full codebase. Deep experience in auth/API platforms on AWS, including moving critical auth services from Lambda to ECS to eliminate cold-start latency.”

sai anuragh Sangoju

Screened

Mid-level AI/ML Engineer specializing in fraud detection, credit risk, and NLP

Dallas, Texas4y exp

“Built and deployed a production LLM-powered university support chatbot on Azure using a RAG pipeline, focusing on reducing hallucinations, improving latency, and handling ambiguous queries via confidence checks and clarification prompts. Also has hands-on orchestration experience (Airflow/Azure Data Factory), including hardening a demand-forecasting ingestion workflow with sensors, retries, and automated alerts, and uses a metrics-driven testing/monitoring approach for reliable AI agents.”

Mark Wlodawski

Screened

Senior Unity Developer specializing in AI/LLM systems and multiplayer VR

Orlando, FL11y exp

“Backend/data engineer focused on AWS-native Python systems: built a FastAPI microservice on ECS/Fargate serving real-time analytics at millions of daily requests with strong reliability (OAuth2/JWT, retries/timeouts, correlation IDs) and autoscaling. Also delivered Glue/PySpark ETL pipelines to curated S3 Parquet/Athena with schema evolution + data quality controls, owned Airflow pipeline incidents, and has a track record of measurable performance and cost optimizations (e.g., ~80%+ query latency reduction; reduced logging/NAT/Fargate spend).”

“Built and deployed a production LLM-powered RAG knowledge system to unify operational/policy information across PDFs, wikis, and databases, emphasizing auditability and low-latency/cost performance. Improved answer relevance at scale by moving from pure vector search to hybrid retrieval with metadata filtering and reranking, and partnered closely with healthcare operations/compliance to define acceptance criteria and human-in-the-loop guardrails.”

Susheel Thimlapur

Screened

Mid-level Full-Stack Engineer specializing in web applications and cloud systems

Bloomington, IN4y exp

“Software developer at Indiana University who built a phishing training platform using TypeScript/React, Node, GraphQL, and DynamoDB, including an admin tool to monitor student completion and send automated reminder emails. Also created TechRent, an IU-only electronics rental marketplace concept focused on improving security by restricting access to university students.”

Ruchi Desai

Screened

Senior Backend Engineer specializing in cloud-native .NET microservices

Frisco, TX8y exp

“Healthcare-focused full-stack engineer who has spent their career in small startup-like teams, owning products end-to-end from business requirements to architecture, implementation, deployment, and support. They’ve built critical hospital-facing systems including patient intake, insurance eligibility, appointment search at scale, and configurable multi-tenant admin platforms using React, Node.js, .NET, AWS, MySQL, and Elasticsearch.”

“New grad focused on AI systems and agent-based development, with hands-on experience using LLMs as a coding partner and building RAG-based document processing workflows. Stands out for practical experimentation with semantic chunking, retrieval optimization, and multi-agent architectures, including redesigning a RAG workflow by adding a reasoning agent to improve response accuracy and reliability.”

“Candidate has hands-on experience at Cognizant building production-grade automation and integration solutions across Python ML services, Java microservices, Kafka, and Selenium-based UI testing. They stand out for a strong reliability mindset—covering failure modes, observability, flaky test hardening, and translating ambiguous payment-system business processes into resilient end-to-end automated workflows.”

“Healthcare-focused ML/AI engineer who has built production voice agents and clinical question-answering systems end-to-end, from experimentation through deployment, observability, and iteration. Particularly strong in making LLM systems reliable in real workflows via RAG, fine-tuning, guardrails, evaluation pipelines, and shared Python tooling; cites ~20% clinical QA accuracy gains and ~40% faster physician decision turnaround.”