Vetted Observability Professionals

“ML/backend engineer with PayPal experience building high-stakes production systems, including a GenAI internal support assistant and a real-time fraud scoring pipeline. Strong in Python/FastAPI, model-serving infrastructure, RAG architecture, and production observability, with clear readiness to transition those backend patterns into a TypeScript stack.”

“Platform/infra engineer with strong ownership of Kubernetes on VMware and day-to-day hybrid on-prem-to-AWS operations. Has hands-on experience automating infrastructure delivery with Terraform/Ansible/CI-CD, and has resolved real production issues spanning CSI storage reattachment during upgrades, vSphere storage-latency performance degradation, and hybrid connectivity/routing failures with improved validation, monitoring, and failover.”

Vignesh Shanmugasundaram

Screened

Junior Software Engineer specializing in full-stack development and applied ML

New York, NY2y exp

“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.”

“Engineer who uses AI in a disciplined, practical way—leveraging it to speed debugging, generate edge-case tests, and improve coverage while retaining ownership of system design and production validation. Has experimented with chained AI tools but prefers simpler workflows when they reduce noise and review overhead.”

“Engineer with a notably mature AI-native development process: uses Claude/Claude Code in a test-first, iterative workflow and has led multi-agent builds across frontend, backend, and testing. Most notably, they led development of an AI voice agent platform, creating custom agent skills and enforcing clear architectural boundaries to deliver a stable, scalable system.”

Alexander Smith

Screened

Junior Software Engineer and Data Scientist specializing in AI/ML systems

California, USA3y exp

“Built production-grade automation and ML/data pipelines at Dun & Bradstreet and ThreadNotion, spanning large-scale document classification, country risk report automation, and resilient Playwright testing for dynamic AI chat workflows. Particularly strong in turning brittle or ambiguous systems into reliable, observable, end-to-end automated platforms.”

Tara Munjuluri

Screened

Junior Software Engineer specializing in full-stack and AI systems

Ames, IA3y exp

“Backend-focused engineer with end-to-end ownership experience on internal platforms at John Deere, including a workforce and skills system that cut manual review time by 40%. Brings a strong reliability and compliance mindset across Java/Python microservices, AWS infrastructure, and production operations, and has also built an LLM-powered RAG system over 1M+ records with emphasis on grounded outputs and observability.”

Varshitha Macherla

Screened

Junior Full-Stack Developer specializing in Java microservices and cloud platforms

Overland Park, KS2y exp

“Full-stack engineer (~2.6 years) with strong Java/Spring Boot backend experience and React/Angular frontend exposure, who has worked on enterprise-scale systems at Dell processing ~1.8M daily transactions/events. Built secure, partner/internal-facing APIs (OAuth2/JWT) across 14 integrations and implemented Kafka-based order/payment workflows with idempotency and sub-700ms processing targets, plus CI/CD and Selenium-based release validation.”

“Backend/platform engineer with significant ownership at GoDaddy, where they built a real-time personalization and decisioning system that drove about $7M in annualized revenue and serves roughly 4M requests per day. Also operates as a solo engineer for a global human-rights legal-tech nonprofit, building the full platform and graph-based matching engine for 700+ partner organizations. Brings a strong blend of production backend rigor, platform thinking, and practical AI orchestration.”

“Senior engineering leader in healthcare AI who combines org scaling with deep hands-on architecture work. At DriveHealth.ai, they helped evolve isolated workflows into a production-grade intelligent platform, standardizing a shared RAG+DCE architecture while leading teams of 50+ across engineering, AI, platform, QA, and DevOps.”

Timothy Chu

Screened

Senior Software Engineer specializing in backend systems and data pipelines

Atlanta, GA11y exp

“Backend-leaning full-stack engineer from Home Depot who operated in small, startup-like teams with end-to-end ownership of critical production systems. Stands out for combining Go/Python backend depth, React/TypeScript collaboration, and strong reliability instincts—improving search latency by 40%, cutting DB latency by 35%, and hardening high-volume data and compliance pipelines.”

“Backend/full-stack engineer (5+ years) with Shopify experience integrating LLM/RAG workflows into production APIs. Owned a Python TensorFlow Serving inference pipeline connected to Java microservices via gRPC, optimizing tail latency at ~10k concurrent load and improving retrieval relevance with embedding and evaluation work. Strong Kubernetes/EKS + GitOps/CI/CD background, including monolith-to-microservices migrations and event-driven streaming patterns.”

“Built and shipped a production generative-AI recipe feature on AWS serverless (Lambda + Bedrock), evolving it post-launch from fully AI-generated outputs to user-guided structured generation based on real usage patterns and system metrics. Emphasizes reliability via prompt constraints plus deterministic validation, with automated/human eval loops and CloudWatch-based observability to manage latency, cost, and output consistency.”

Ruturaj Ghatage

Screened

Junior Software Engineer specializing in full-stack, cloud infrastructure, and applied AI

Herndon, Virginia2y exp

“Master’s student at UC San Diego who built an LLM-powered healthcare chatbot for patient history-taking and sepsis-related output, using a Node.js backend integrated with FastAPI for RAG/LLM interactions and a Flutter client. Also has healthcare AI startup experience deploying on AWS (ECS/Terraform/Docker) and implementing Kubernetes autoscaling to improve efficiency and reduce costs, with strong iterative evaluation in collaboration with a physician.”

“Data/analytics engineer who built and operated a DocuSign-based real-time analytics platform end-to-end, processing 20–50k webhook events/day with ~99.5% reliability. Strong in idempotent event processing, schema-evolution-safe ingestion (raw JSON + dynamic parsing), and serving data via versioned, low-latency REST APIs with solid CI/CD and observability.”

“Second most active contributor at Daice Labs, owning a production AI-powered software development collaboration platform’s end-to-end execution infrastructure (TypeScript/Next.js backend, Node.js CLI, shared libs). Built the full multi-agent pipeline (planning/codegen/summary), Supabase-backed context assembly and realtime state, Git/GitHub automation, and a provider-agnostic LLM abstraction with strict Zod validation and retries, backed by extensive tests and design specs.”

“ML/GenAI engineer with strong end-to-end production ownership across predictive ML, RAG systems, and LLM routing. They pair solid platform engineering skills with measurable business impact, including 15% churn reduction, 35% support ticket deflection, 45% GenAI cost savings, and a shared inference library that cut deployment time from weeks to days.”

Michael Matonte

Screened

Senior Backend Engineer specializing in distributed systems and AI-enabled platforms

Jersey City, NJ7y exp

“Backend engineer with end-to-end ownership experience in high-stakes environments spanning Citibank and industrial operations. They built an internal banking platform that automated complex entitlement workflows across thousands of business units with an 80% reduction in redundant processing, and they are now applying AI through OpenAI-powered agent workflows with RAG, vector databases, and security controls.”

“Engineering leader focused on large-scale enterprise SaaS and MDM platforms, with experience modernizing monoliths into microservices, improving reliability, and scaling systems to support 15M devices across AWS and Azure. Stands out for combining deep platform architecture work with strong org-building: managed teams up to 45 globally and built a 0-to-1 platform services team to 22 people in under a year.”

Harsh Sanas

Screened

Intern Full-Stack Engineer specializing in AI and distributed systems

Los Angeles, CA2y exp

“Full-stack product engineer who has designed and shipped production web experiences in EV charging, trading, automotive companion apps, and AI systems. Stands out for owning user-facing React experiences through backend integration and production monitoring, with a strong bias toward reliability in real-time and high-stakes workflows. Also has early-stage Scale AI experience building a Text-to-SQL agent stack with Python, PostgreSQL, Redis, Kafka, and AWS.”

“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).”

“Backend/data engineer from ASML who modernized a legacy SAS-based statistical processing system into a cloud-native AWS platform (Lambda/FastAPI, Step Functions/EventBridge, Glue, S3/RDS) with strong reliability and data-quality practices. Demonstrated measurable performance wins (RDS query reduced from 90+ seconds to <5 seconds) and hands-on incident ownership for production ETL pipelines.”

“Amazon experience integrating LLM-powered chat automation into Amazon Connect contact-center workflows, taking prototypes to production with compliance-minded guardrails, schema/policy validation, and robust fallbacks. Regularly supports rollout and adoption via developer workshops, integration guides, and customer calls, with strong production triage and observability practices.”

“Backend engineer with experience at Amazon building an API-driven service (APS) for large-scale prompt optimization jobs using AWS Step Functions, Batch/Fargate, DynamoDB, and S3, emphasizing idempotency, observability, and secure execution boundaries. Also led a multi-tenant enterprise policy/configuration backend refactor at MAMIT Cyber with versioned schemas, shadow writes, feature-flagged rollout, and PostgreSQL RLS-based tenant isolation.”