Vetted Observability Professionals

“Backend-focused TypeScript/Node.js engineer who owned a production microservice for transactional workflows in a React + microservices platform, integrating REST and Kafka event processing. Emphasizes operability and correctness (idempotency keys, exponential backoff retries, DLQs, centralized logging/metrics/alerts) plus strong API DX via versioning and Swagger/OpenAPI with improved error contracts based on developer feedback.”

“AI research engineer who has built and tested LLM agents end-to-end, including a Telegram real-time voice-to-typing assistant integrated with calendar scheduling. Emphasizes production concerns (security via mic-triggered activation, multi-model fallbacks, monitoring) and agent predictability using a GPT-3.5-based critic plus structured outputs (Pydantic) and ReAct-style orchestration.”

“Engineering leader/player-coach who redesigned A/B testing and observability infrastructure for real-time audio/video features at 90M+ DAU scale, using Kafka-based ingestion and horizontally scalable aggregation for near real-time metrics. Managed 10–15 engineers, drove >20% infrastructure cost reduction without user-experience regressions, and led major peak-traffic incident response with lasting reliability improvements (load testing, capacity planning, alerting, pre-mortems).”

Likhith Kumar Yaramala

Screened

Junior Backend/Cloud Software Engineer specializing in microservices and DevOps

India2y exp

“Cloud/DevOps-focused engineer with strong Linux production operations experience, deploying microservices to AWS on Docker/Kubernetes. Has built and operated secure CI/CD (GitHub Actions/Jenkins) and Terraform IaC workflows with approvals, remote state, and drift detection, and has hands-on incident recovery experience in containerized environments; limited direct IBM Power/AIX/PowerHA exposure.”

Ashritha G

Screened

Mid-Level Software Development Engineer specializing in distributed systems and cloud microservices

USA3y exp

“Software engineer with enterprise, customer-facing delivery experience across Outlier AI and Wipro—builds and productionizes workflow and integration solutions with a strong focus on real-world performance and reliability. Delivered a Firestore/Redis-backed real-time pipeline that cut page load times by 20% and held consistent performance across 10,000+ sessions, and has hands-on production incident experience stabilizing high-traffic microservices via caching, indexing, and safe canary deployments.”

Sam Fish

Screened

Senior Full-Stack Engineer specializing in web platforms, cloud infrastructure, and data systems

Culver City, CA5y exp

“Full-stack/product-leaning engineer who owned an end-to-end AI Tutor feature (Claude-powered) shipped simultaneously to iOS/Android/web via Expo, with Cloudflare Workers backend and PostHog analytics. Built the company’s GitHub-based CI/CD to coordinate app store releases with backend blue/green deployments. Also has significant data engineering experience (including ~8TB/day workloads) using dbt/Fivetran plus sharding and hashing-based diffing for correctness.”

Abhishek Ingle

Screened

Junior Full-Stack & AI Software Engineer specializing in React/Next.js and LLM systems

Bloomington, IN2y exp

“Backend engineer with hands-on experience building low-latency, high-concurrency real-time chat on AWS (Node.js/Socket.IO/MongoDB) and improving reliability under unstable networks, contributing to ~40% user adoption growth. Also built FastAPI-based AI assistant context retrieval (RAG) APIs with embeddings/vector search, and has strong production experience in rate-limit handling, async refactors with safe rollout, and Supabase Auth/RLS optimization.”

Cody Feda

Screened

Principal Full-Stack Engineering Leader specializing in forward-deployed SaaS platforms

San Francisco, CA14y exp

“Hands-on builder and workshop facilitator focused on getting LLM/agentic and dev-tools products from prototype to production. Has delivered iterative production deployments for a construction client (estimating/invoicing on EC2), debugs LLM regressions with monitoring plus prompt optimization, and has facilitated multiple developer workshops while helping founders improve onboarding and time-to-first-success using tools like PostHog.”

“Independent product builder with several live startups spanning transit, gaming, and SaaS/API tools. Notably built a Cyprus bus routes app before transit data was broadly available on Google Maps, and is currently maintaining products like CountriesDB and HolidayDB while pursuing SEO-led growth and validating ideas solo before expanding.”

Pranav Zagade

Screened

Junior Software Engineer specializing in full-stack and AI systems

Arizona, USA3y exp

“Built and shipped an LLM-powered support agent for Community Dreams Foundation that automated intake and Jira ticket creation using RAG, structured outputs, and strong production guardrails. Demonstrated practical production AI experience with 92% routing accuracy, 98% uptime, and a 31% improvement in first-response accuracy, plus hands-on work in observability, evals, idempotency, and failure handling.”

Kunal Sanghvi

Screened

Mid-level Data Engineer specializing in AI, NLP, and LLM systems

USA3y exp

“Built and deployed a production AI customer support chatbot at Unique Design Inc. using FastAPI, AWS, Docker, and retrieval-based grounding on internal documents. Stands out for hands-on ownership across discovery, deployment, incident debugging, and post-launch iteration, with a strong focus on making LLM systems reliable and safe in real business workflows.”

Meghana Divve

Screened

Mid-level Full-Stack Software Engineer specializing in cloud-native enterprise applications

Tampa, FL6y exp

“Built and launched a production internal AI support assistant at CompuCom, focused on reducing time spent searching across systems by combining retrieval, internal tool use, and grounded LLM responses. Stands out for pragmatic zero-to-one execution: scoped the product in phases, prioritized safety over premature autonomy, and iterated using real user feedback to improve relevance, usability, latency, and cost.”

“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.”

“Python/backend engineer with a data-systems focus who actively integrates AI tools like ChatGPT, Copilot, and multiple coding assistants into development workflows. Stands out for using AI pragmatically across coding, debugging, SQL, testing, and documentation while maintaining hands-on ownership of validation, implementation, and code quality.”

“Built a production LLM/RAG document extraction and game/quiz content workflow using LLaMA 2, LangChain/LangGraph, and FAISS, achieving ~94% accuracy and reducing turnaround from hours to minutes. Demonstrates strong applied MLOps/orchestration (CI/CD, MLflow, Databricks/PySpark), robust handling of noisy/variable document layouts (layout chunking + OCR fallbacks), and practical reliability practices (human-in-the-loop routing, drift monitoring, A/B testing).”

“Backend/data engineer with hands-on production experience across Python/Flask microservices and AWS serverless/data platforms (Lambda, DynamoDB, S3, Glue/PySpark). Demonstrated strong reliability and operations mindset (JWT/RBAC, retries/timeouts/circuit breakers, CloudWatch/SNS alerting) and measurable performance wins (SQL report runtime cut from 10 minutes to 30 seconds). Seeking ~$150k base and cannot travel for onsite meetings for the next 5–6 months due to family medical constraints.”

“AI/LLM engineer who has shipped production RAG chatbots using LangChain/OpenAI with FAISS and FastAPI, focusing on real-world constraints like context windows, concurrency, and latency (reported ~40% latency reduction and <2s average response). Experienced orchestrating AI pipelines with Celery and fault-tolerant long-running workflows with Temporal, and has applied NLP model tradeoff testing (Word2Vec vs BERT) to drive measurable accuracy gains.”

“At Unar Labs, built and operationalized LLM capabilities inside a cloud-native SaaS product, emphasizing production reliability (fallbacks, observability, cost/latency/quality monitoring) and iterative improvement from user feedback. Also acts as a customer-facing technical lead—running developer demos/workshops and supporting sales through discovery, pilots/POCs, and technical walkthroughs to drive production adoption.”

“Built and productionized an agentic RAG assistant using Ollama + LangChain + MCP + ChromaDB to speed up and standardize access to operational knowledge from tickets and runbooks. Focused on real-world reliability: mitigated timeouts/latency with retries and concurrency limits, improved retrieval via chunking/embedding iteration, and reduced hallucinations through citation-grounding and confidence-based abstention. Also partnered with non-technical ops staff to deliver anomaly detection/monitoring by translating operational needs into model signals, thresholds, and alerting logic.”

“Software engineer experienced delivering customer-facing, real-time industrial monitoring dashboards (motors/shafts/turbines) by partnering directly with end users to refine charts, alerts, and performance. Strong in API/platform integrations and production troubleshooting—uses feature flags, logging, validation/mapping, containerization, and performance testing to keep systems stable while iterating quickly.”

“DevOps engineer (State Farm) with hands-on ownership of Python backend services and data pipelines, deploying microservices and workers on Kubernetes using GitOps (Argo CD). Has led complex cloud-to-on-prem/hybrid migrations with staged cutovers and rollback planning, and built Kafka-based real-time streaming pipelines with schema governance, autoscaling, and strong observability.”

“LLM/RAG engineer who has built and shipped production assistants, including a RAG-based teaching assistant (Marvel AI) using LangChain/LlamaIndex/ChromaDB with OpenAI embeddings and Redis vector search, achieving ~30% accuracy gains and ~35% latency reduction. Also deployed FastAPI services on Google Cloud Run with observability and prompt-level monitoring, and partnered with non-technical ops stakeholders to deliver an internal policy-document RAG assistant.”

“Founder/CTO-type candidate with deep exposure to the VC/accelerator ecosystem (Open Angel Forum; HAX/SOSV) and a track record of raising capital, including a $1M seed round in 2017. Built ventures spanning IoT wearable/HUD SaaS for industrial hygiene monitoring and institutional Bitcoin mining/distributed compute infrastructure, and ran a venture from 2017–2025.”

“Data engineer who has owned production pipelines end-to-end, ingesting 50–100 GB/day from APIs/S3 and near-real-time Kafka into Snowflake with strong data quality gates (Great Expectations/dbt) and Airflow-based reliability (SLAs, alerting, dashboards). Also built a Snowflake-backed REST data API with caching/pagination and versioned endpoints, and designed a compliant, scalable web-scraping system with anti-bot handling and safe backfills.”