Vetted Algorithms Professionals

“Software engineer with hands-on experience shipping production AI agents and end-to-end ecommerce workflows. They built a customer support automation agent with strong guardrails and evaluation practices, then improved it post-launch using real user data to cut latency ~30% and token cost ~25%. Also drove a zero-to-one self-serve order modification product across React UI, backend services, and cross-functional alignment.”

“Backend Python engineer focused on production-grade automation and reliability, with hands-on experience designing scalable API systems on PostgreSQL and making pragmatic architecture calls (modular monolith over premature microservices). Demonstrated measurable performance wins (50–60% latency reduction) and strong operational rigor via observability, incremental rollouts/feature flags, and security patterns like JWT + RBAC + database row-level security.”

“University Rover Competition autonomous-systems lead who architects and debugs a full ROS 2 autonomy stack (Nav2, vSLAM, EKF fusion) and backs it with strong engineering hygiene (Docker + GitHub Actions CI running headless Gazebo and colcon tests). Also has industry-facing ROS 2 hardware integration experience, building a ros2_control plugin for a Unitree G1 arm using CycloneDDS and optimizing real-time behavior via QoS tuning.”

“AI/LLM engineer who built a production RAG-based Text2SQL engine using Qdrant, including creating the underlying business/DB documentation, generating a test dataset, and designing detailed SQL-quality metrics for validation. Also partnered with non-technical stakeholders on a speech recognition project to prioritize medical terminology, improving accuracy through targeted corpora, lookup-table correction, and fine-tuning with a modified loss function.”

“Master’s-program backend engineer with strong Java/Spring Boot industry experience who also owned a Python analytics service (Flask/Postgres, JWT, Celery/Redis) and optimized large-dataset performance via SQL/batching. Has hands-on Kubernetes microservices deployment and GitLab+Terraform CI/CD/GitOps workflows, plus experience supporting phased on-prem to AWS migrations and building Kafka-based real-time streaming pipelines.”

“Software engineer with connected-car/automotive production experience who owned an end-to-end remote door lock/unlock feature and introduced unit testing (GTest) plus rig/simulator validation. Also built and productionized an AI-native AWS cloud cost assistant (Lex + GPT-based LLM + Lambda + RAG/vector DB) with guardrails and achieved 94% evaluation accuracy. Helped replace a third-party solution with an in-house build, saving the company ~€9M.”

“Built and shipped an AI-powered driver ranking/assignment system at AffirmoAI using LLM intent classification + RAG over pgvector/Postgres, served via FastAPI with a React UI that explains scores. Drove measurable improvements through optimization and iteration (latency down to <800ms, adoption 60%→90%+) and implemented rigorous eval loops with dispatcher ground truth plus cold-start handling for new drivers.”

Harshini Jonnala

Screened

Senior Backend Software Engineer specializing in distributed systems and cloud microservices

Hyderabad, India2y exp

“Backend engineer with NTT Data experience building Java/Spring Boot services for product-data ingestion, including Kafka-based asynchronous pipelines and Redis read-through caching. Also built a personal RAG system deployed on Google Kubernetes Service using FastAPI, LangChain, and Pinecone with multi-tenant data isolation; holds a Master’s background in Machine Learning.”

Sowmya Kasu

Screened

Mid-Level Software Engineer specializing in Healthcare IT and cloud-native microservices

Northridge, CA6y exp

“Backend/ML engineer with healthcare experience at Kaiser Permanente building HIPAA-compliant Java/Spring Boot + GraphQL APIs integrated with Epic HealthConnect, including hands-on reliability/performance debugging using Prometheus/Grafana and resolver-level N+1 elimination. Also built an end-to-end malaria parasite detection ML feature (CNN/R-CNN) with evaluation, guardrails, and workflow integration, and has experience designing robust state-machine-based automation with retries, DLQs, and alerting.”

Jonathan Dearborn

Screened

Senior Gameplay Engineer specializing in VR and cross-platform gameplay systems

14y exp

“Unreal Engine (C++/Blueprint) gameplay systems engineer who shipped major VR social-sports features in Orion Drift, including a wieldable items system that became the game’s primary IAP revenue driver and supported 75-player lobbies via careful replication/performance tradeoffs. Also built the station/server browsing system to steer players into lively lobbies and implemented backend-to-frontend tech tree infrastructure using Unreal DataTables, plus tuned standout mechanics like rideable rockets and Driftball projectile/ball behavior through frequent playtests.”

“Built and shipped LLM-enabled decision systems focused on real production reliability rather than chatbot demos, including a multimodal radiology retrieval platform with 28% relevance gains and 35% lower latency. Also architected a 4-agent employee analytics workflow with structured outputs, traceable orchestration, and strong safeguards for messy real-world data.”

“Built and shipped a production LLM-powered email automation agent for procurement that ingests emails/attachments, classifies requests via rules+embeddings+LLM fallback, enriches responses with SAP inventory data, and generates templated replies. Architected it as an event-driven, idempotent Azure Functions/Queues pipeline with schema-constrained outputs, confidence gating, retries/circuit breakers, and Application Insights monitoring—cutting turnaround time from 4–7 days to near real-time while maintaining zero downtime.”

Kush Shah

Screened

Junior Full-Stack Software Engineer specializing in web apps and automation

Remote, USA3y exp

“Backend engineer with hands-on experience building an AI-powered document processing pipeline for insurance workflows from design through deployment and production support. They combine LLM-based extraction with rule-based validation, retries, and observability, showing a pragmatic approach to making AI systems reliable in high-stakes environments.”