Vetted Monitoring Professionals

“Backend/platform engineer who built an LLM-driven QA automation system (“mockmouse”) using a Flask orchestration microservice, Socket.IO real-time updates, Redis caching, and strict Pydantic schemas to turn prompts into reliable action graphs and automated browser tests. Has hands-on Kubernetes delivery experience (Docker/Helm/Jenkins) and has supported large migration programs, validating ETL cutovers with 1M+ synthetic records and rigorous output comparisons; also built event-driven monitoring/anomaly detection streaming into Grafana.”

“IBM Power/AIX engineer who has owned a large production estate (20+ Power9/Power10 frames and 400+ LPARs) with vHMC and dual-VIOS HA. Has hands-on incident recovery experience (NPIV/RMC issues, LPM restores) and PowerHA failovers, plus modern DevOps exposure using Terraform on AWS and CI/CD with GitHub Actions/Jenkins (including deploying AI/RAG and vision workloads).”

“Backend/AI engineer who built and refactored a production RAG system over IRS Form 990 filings for 60 nonprofits, using a dual-path architecture (deterministic financial ranking + TF-IDF semantic retrieval) to keep latency sub-2s and reduce hallucinations. Demonstrates strong API craftsmanship in FastAPI (contract-first, OpenAPI-driven) plus production-grade security for multi-tenant systems (JWT, RBAC, Supabase-style RLS) and careful migration practices (feature flags, traffic mirroring, incremental rollout).”

“Full-stack engineer from an early-stage AI SaaS startup who owned and shipped a production AI-powered PDF document chat and sharing feature end-to-end (React/TS + Node + Postgres on AWS). Demonstrates strong product thinking through layered success metrics and tight feedback loops, plus hands-on reliability/observability work (CloudWatch, structured logging, alarms) and robust ingestion pipeline patterns (idempotency, retries, reconciliation).”

Haneesh Kapa

Screened

Junior AI Full-Stack Engineer specializing in LLM automations and RAG systems

Nashua, NH2y exp

“Built and shipped a production LLM-powered customer support assistant using a Python/FastAPI backend with RAG (embeddings + vector search) over internal docs and product/operational data. Instrumented the system with logging/metrics and ran continuous eval loops; post-launch improvements focused on retrieval quality (chunking/ranking) and performance/cost tradeoffs (query classification, caching, validation guardrails).”

HarshelSrivatsava Srivatsava

Screened

Intern Full-Stack Engineer specializing in AI-powered SaaS products

Birmingham, AL1y exp

“Solo builder of OGym, shipping production AI features for gyms that turn member behavior/health data (workouts, attendance, nutrition, payments, device metrics) into prioritized, actionable owner and member insights. Designed and implemented FastAPI backends, PostgreSQL-based RAG workflows, guardrails (RBAC/validation/rate limiting), and real-user evaluation loops, with a strong focus on latency/cost optimization and reliable data pipelines.”

Habtom Desta

Screened

Mid-level Data Scientist specializing in Python, ML, and BI dashboards

Dallas, TX5y exp

“Data/NLP practitioner who builds production-oriented pipelines for unstructured text: entity extraction, topic modeling (LDA/BERTopic), and semantic search using Sentence-BERT embeddings with FAISS. Emphasizes rigorous evaluation (coherence/silhouette + manual review), entity resolution with validation, and scalable workflow orchestration using Airflow/Prefect with Spark/Dask.”

“Backend engineer with production ERP experience deploying microservices and improving performance/reliability using a metrics-driven approach (logs, latency, error rates). Has hands-on cloud/hybrid operations across AWS and Azure with Docker/Kubernetes, and has resolved real-world mobile sync issues by tuning timeouts/retries and reducing payload sizes. Builds configurable Python services to deliver customer-specific behavior without destabilizing the core codebase.”

“Software engineer with demonstrated production performance wins (37% latency reduction) through SQL optimization, backend API redesign, and disciplined rollout practices (staging, feature flags). Experienced debugging distributed pipeline issues across infrastructure layers (memory pressure and network timeouts) and building AWS-based systems (Lambda + RDS) to handle request spikes, including work on a business-focused chatbot.”

“Built and deployed a production RAG-based internal document Q&A system using LangChain, vector search, and a dockerized FastAPI LLM service. Focused on reliability by systematically reducing hallucinations and improving retrieval through prompt grounding/abstention strategies, chunking and top-k tuning, and iterative evaluation with logged metrics and manual validation.”

“Full-stack system analyst/programmer at PeakPlay Sports (startup) who built an AI "coach" product end-to-end in ~2 months, using a LangGraph-orchestrated multi-agent architecture with a FastAPI backend. Shipped production RAG grounded in athlete history (OpenAI embeddings + vector store) with guardrails and a structured eval loop (golden set + LLM-judge + human review) to improve engagement and reduce hallucinations.”

“Full-stack developer who owned and shipped an end-to-end web application for LeafNBeyond (React/Node/Postgres), deployed to production at leafnbeyond.com, with reported 35% sales growth and strong UX feedback. Also built Azure-based ETL pipelines using lakehouse/medallion architecture with validation and retry logic, and has AWS fundamentals from a master’s coursework (EC2, RDS, IAM, load balancing).”