Vetted Monitoring Professionals

“Senior infrastructure/platform engineer with deep IBM Power/AIX (Power9, VIOS, HMC, LPAR/DLPAR) and PowerHA production ownership at scale (40 frames / ~300 LPARs), including hands-on outage recovery and performance tuning. Also delivers modern DevOps/IaC capabilities—CI/CD for Kubernetes microservices and Terraform-based multi-account AWS (EKS/VPC/IAM/RDS) with drift detection and safe rollout controls.”

“Senior Engineering Manager with large-scale platform/API ownership at eBay, leading a globally distributed team and redesigning Order public APIs used by external developer ecosystems at ~800M requests/day, delivering 84% performance gains and reducing compute by ~300 VMs. Also led Google CRES ETL migration work on GCP, creating reusable Python libraries to standardize configuration across 10 integrations and improve developer productivity.”

Brian Sanders

Screened

Senior Backend Engineer specializing in GenAI, LLMs, and scalable data pipelines

Chicago, IL12y exp

“Backend/ML platform engineer from Snapsheet who owned production Python services and data pipelines for insurance claims, including an AI document classification/summarization FastAPI service on ECS/Fargate processing 1M+ documents/year. Strong in AWS infrastructure (Terraform, CI/CD, secrets/IAM, autoscaling), Glue/PySpark ETL with schema evolution controls, and legacy SAS-to-microservices modernization with safe, feature-flagged rollouts and measurable performance wins.”

“Entrepreneurial builder and former founding CTO of a Y Combinator startup that was acquired. Now in the planning stage for a new venture (considering edtech, media creation, and food/"intelligent servicing"), emphasizing early design partners, rapid testing, and iteration to validate ideas.”

“Backend-focused engineer with Walmart Global Tech experience building shipment and seller workflow systems using Spring Boot, GraphQL, Kafka, and async processing. Stands out for improving bulk label API performance by 60-75%, designing item-level partial-failure workflows that improved user clarity, and also exploring AI-powered debugging/RCA platforms with Java, Python, LangChain, and LLM integrations.”

“Backend/data engineer with experience at Nike building high-volume order orchestration and validation APIs using FastAPI microservices on AWS EKS with Kafka, Redis, and Postgres. Strong in production reliability (timeouts/retries/idempotency), GitOps (Argo CD) + Terraform deployments, and data pipelines (AWS Glue/S3), with hands-on incident ownership and legacy modernization into API-driven services.”

“LLMOps-focused technical leader who took an LLM use case from prototype to production for a non-technical customer by combining trust-building and structured enablement with a robust AWS/Kubernetes-based MLOps stack. Built observability and rollback mechanisms (Grafana + MLflow) to troubleshoot in real time, and scaled delivery by hiring a 5-person team while partnering with sales to manage expectations and drive adoption across departments.”

Shant Mardigian

Screened

Executive Engineering Leader specializing in scalable streaming, media supply chain, and AI operations

19y exp

“Tech executive with Disney experience who has repeatedly scaled and restructured engineering organizations (from 4 to 30 and up to 100+), using OKRs/KPIs to drive business-aligned roadmaps. Hands-on with architecture and platform strategy, including adopting MongoDB Atlas to centralize transactional data and building shared core services (security/permissions, auditing, compliance) to increase product velocity across distributed teams.”

“Full-stack engineer with strong startup experience across small distributed teams, owning everything from React UIs to Go services and Python data pipelines. He stands out for designing stable APIs and data models in ambiguous compliance-heavy environments, with measurable impact including a 38% reduction in manual cleanup and a 27% drop in customer-visible incidents. He has also worked deeply in B2B SaaS at Ashby, supporting mission-critical recruiting workflows where data correctness, permissions, and integration stability were essential.”

Chinedu Enwere

Screened

Mid Software Engineer specializing in distributed backend systems

Redmond, WA4y exp

“Engineering candidate deeply embedded in AI-native development, currently using tools like Cursor and Claude Code to generate most of their code and building internal agents for on-call monitoring, anomaly detection, and automated incident mitigation. Particularly interesting for teams exploring AI-first engineering workflows, multi-agent development setups, and operational automation at scale.”

Xun He

Screened

Staff Software Engineer specializing in backend systems and security engineering

25y exp

“Senior backend/infrastructure engineer with experience at Microsoft, DocuSign, Pinterest, and Harness, focused on security and developer productivity systems. Stands out for building Pinterest's first vulnerability management platform from scratch for a 100,000-EC2 production environment and driving major performance gains, while also delivering CI/Docker infrastructure and Python test intelligence features shaped by customer demand.”

Pavithra Kollu

Screened

Mid-level Machine Learning Engineer specializing in LLMs, RAG, and search systems

San Francisco, CA5y exp

“Backend/ML infrastructure engineer with experience at Perplexity and Meta building production evaluation, monitoring, and retrieval systems for AI search, autonomous agents, and LLM-powered workflows. Particularly strong in turning messy manual quality-review processes into reusable Python/FastAPI automation with measurable impact, including major gains in search relevance, latency, and grounded answer quality.”

“Engineering/data platform leader from FLYR (airline ML forecasting and automated pricing) who built scalable ingestion/ETL and a canonical data model to onboard airlines with highly heterogeneous source systems. Created a golden-metrics layer for airline KPIs and implemented monitoring/backfill capabilities, cutting onboarding time by 50%+ while improving SLA performance and controlling cloud/ML training costs through stronger data quality gates.”

“Engineering/product leader with repeated experience defining roadmaps and delivering platform migrations across SaaS and enterprise contexts. Scaled an education system from two districts to 2M+ students statewide, and led an Elasticsearch-based architecture for an internal Disney product serving ~100K employees. Known for rigorous execution (RAID/OKRs/sprints), strong stakeholder communication (weekly AI summaries), and empowering teams via end-to-end ownership and customer-connected development.”

Vinay Ramrupe

Screened

Mid-level AI/ML Engineer specializing in LLM and enterprise generative AI

San Francisco, CA5y exp

“ML/AI engineer focused on taking LLM systems from experimentation to reliable production, including enterprise copilot and RAG-based knowledge retrieval use cases. Stands out for combining data pipelines, model training, inference optimization, automated evaluation, and safety guardrails, with cited impact including 20% throughput gains and 30% less manual evaluation effort.”