Vetted Observability Professionals

Kirill Aristarkhov

Screened

Junior Software Engineer specializing in full-stack, AI/ML systems, and game development

Santa Barbara, CA3y exp

“Full-stack engineer (React/TypeScript + Bun/Node-like backend) who recently rebuilt a terminal-based chat UI, implementing custom Markdown lex/parse/render and a typewriter-style streaming renderer while optimizing React DOM growth for ~50% faster performance. Has startup experience making high-ownership decisions under ambiguity and rapidly integrating multiple external AI/tooling services (5–6 in a week) with fallback strategies for flaky dependencies.”

Raj Patel

Screened

Junior Machine Learning Engineer specializing in LLMs and RAG systems

Remote, USA1y exp

“Production-focused applied ML/LLM engineer who has deployed an LLM-powered RAG assistant and improved reliability through rigorous retrieval evaluation (recall/MRR), reranking, and guardrails that prevent confident wrong answers. Experienced running containerized ML/LLM services on Kubernetes (including AWS-managed layers) with CI/CD and observability, and has delivered a real-time predictive maintenance system using streaming sensor data and time-series anomaly detection in close partnership with maintenance teams.”

Sumanth Gottipati

Screened

Mid-level Full-Stack Software Engineer specializing in cloud-native microservices and FinTech

New York, NY4y exp

“At Delta Airlines, built and shipped a production LLM-powered semantic search/troubleshooting assistant over maintenance logs and operational documentation using OpenAI embeddings and a vector database. Implemented hybrid ranking, query enrichment, and structured filters to improve relevance ~35% while optimizing latency via caching and vector tuning. Also designed a scalable Kafka + AWS (Lambda/SQS) ingestion pipeline with strong reliability/observability and an eval loop using real engineer queries and human review.”

Damaris Villa González

Screened

Entry Mechatronics Engineering student specializing in robotics and embedded control

Guadalajara, México1y exp

“Hands-on builder with projects spanning IoT/backend data systems and robotics: created a flow meter pipeline that sends readings over WiFi to a database and web server with real-time averaging and easy data download. Also diagnosed a hard-to-catch AGV/AMR reliability issue during long autonomous runs (battery voltage drop causing sensor failures) and mitigated it with voltage monitoring and a buffer circuit; familiar with Python/FastAPI API best practices (Pydantic, async, OAuth2/JWT).”

Srichandan Kota

Screened

Senior Full-Stack AI Engineer specializing in Generative AI and FinTech

Minneapolis, MN6y exp

“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.”

Israel Sodano

Screened

Principal/Staff Software Engineer specializing in distributed systems and FinTech platforms

São Paulo, Brazil13y exp

“Brazil-based tech leader who helped build Bankly’s end-to-end Banking-as-a-Service platform—delivering a plug-and-play solution in 6 months and architecting for ~5k requests/second; Bankly later became the largest BaaS in Brazil. Also runs a closed, personal-capital private trading group focused on automating investment decisions with AI trading bots, but is currently prioritizing international career growth over entrepreneurship.”

Ruturaj Dixit

Screened

Junior Data Scientist specializing in AI/ML and product analytics

New York, NY2y exp

“Applied ML/data scientist who has owned backend-heavy AI systems end-to-end, including a market-signal platform on FastAPI/AWS and rapid MVP delivery in medical computer vision. Particularly interesting for teams needing someone who can combine model development, backend APIs, production debugging, and pragmatic low-latency architecture decisions.”

Ramya Sree Kanijam

Screened

Mid-level Software Engineer specializing in backend systems, cloud, and AI pipelines

Remote, USA3y exp

“Built and owned an end-to-end AI-driven content enrichment pipeline for a news workflow, using n8n, LLM agents, and external APIs to automate ingestion, deduplication, categorization, and approval routing. Stands out for production-minded AI systems work: they improved reliability with schema validation, retries, idempotency, and monitoring, while automating 90% of processing and cutting duplication errors by 95%+.”

“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.”

Julian Thong

Screened

Senior Software Engineer specializing in FinTech platforms

San Francisco, CA4y exp

“Senior software engineer with deep experience in private markets platforms, spanning full-stack product development and complex backend modernization. Notably helped migrate a core IOI processing system from a Rails monolith to .NET microservices with Kafka-based event sequencing, and contributed to an Offerings platform that drove 50% faster transactions for investors and operations teams.”

Dishank Kailash Oza

Screened

Mid-level Full-Stack Software Engineer specializing in distributed cloud systems

Santa Clara, CA3y exp

“Engineer with a thoughtful, production-oriented approach to AI-assisted development, including multi-agent workflows for planning, coding, review, testing, and debugging. Stands out for treating AI systems like distributed pipelines with explicit interfaces, validation layers, and guardrails to improve reliability and reduce hallucinations.”

Dinesh Guguloth

Screened

Mid-level Software Engineer specializing in full-stack cloud-native applications

New York, NY4y exp

“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.”

“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.”

“AI/full-stack product builder who has shipped agentic workflow tools in nonprofit and hospital settings, with a strong focus on confidence thresholds, human escalation, and production evals. They stand out for turning ambiguous operational problems into measurable outcomes, including 58% lower manual triage time, 45% faster reporting, and dispatch automation that resolved about 82% of requests autonomously.”

“Built and evolved the backend-adjacent platform for a self-custody Bitcoin wallet used by 2000+ users, emphasizing security-first architecture that avoids custodial risk by keeping private key operations client-side. Demonstrates strong API reliability and security depth (FastAPI contract-first design, idempotency, state-machine modeling, JWT/device-aware auth) and has reduced production failures by hardening signing flows against poor connectivity.”

“Robotics software engineer (2 years) at a startup building a universal fleet management system, owning core integrations and real-time data pipelines for heterogeneous AMR/AGV fleets. Implemented Kalman-filter-based collision prediction integrating RTLS for human-driven forklifts, built MQTT microservices aligned with VDA5050, and is now architecting a PostGIS-backed path-planning service for dynamic, traffic-aware routing with future ML optimization.”

“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.”

“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.”

“LLM-focused engineer with end-to-end experience shipping an OpenAI-powered edtech teacher assistant into production, using Humanloop-driven prompt iteration, rigorous observability (Datadog), and A/B testing tied to real learning metrics (25% comprehension lift). Also led adoption-driving technical demos at SiriusXM (event-driven AWS Lambda/Kotlin/CDK pipeline cutting processing from 24 hours to seconds) and partnered with sales at Spresso.ai to close eCommerce SDK deals and boost activation from 40% to 85%.”

“Built and owned UPS tracking/reporting and operations workflow dashboards, delivering customer-facing APIs and real-time React/TypeScript UIs backed by .NET Core. Experienced in high-volume microservices using IBM MQ/Azure Service Bus with strong reliability patterns (idempotency, retries, DLQ) and Azure-based observability, plus performance tuning across frontend and SQL-backed services.”

“Software engineer with hands-on experience building and maintaining a React accessibility utility/component library (open-source-style) used across university portals, emphasizing WCAG 2.2 compliance, robust focus/keyboard behavior, and Jest/React Testing Library coverage. Also built and maintained .NET Core microservices at the Florida Department of Transportation, including integrating AI-driven features, with strong ownership around observability, incident response, and performance-focused refactoring.”

“Built and deployed a production AI “term explainer” agent that adapts explanations to beginner/intermediate/expert users by combining multi-step LLM reasoning with grounded Wikipedia retrieval. Owns end-to-end agent orchestration (smolagents/Python), reliability patterns (fallback across LLM providers, retries, guardrails), and observability/metrics-driven evaluation; also partnered with a non-technical researcher to deliver a plain-language research assistant agent.”

“Built and deployed a production RAG-based AI assistant for sales reps to unify access to product info, pricing, and internal documents across multiple systems. Implemented ETL pipelines for normalization/chunking/embeddings, integrated the assistant into internal React/TypeScript UIs with user-specific context, and enforced security with private vector storage and permission-filtered retrieval.”