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Nicholas Lu

Site Reliability Engineer
Nicholas Lu is a goal-oriented engineer focused on ML systems and distributed architecture. He works across the MLOps lifecycle, building and maintaining data and model pipelines using Airflow, Spark, Kafka, and Kubernetes. His experience includes deploying containerised workloads with Docker, managing infrastructure via Terraform, and implementing CI/CD with Jenkins, GitLab, and Bitbucket Pipelines. He supports site reliability through monitoring, automation, and incident response in cloud environments (AWS, GCP, Azure, Alibaba Cloud). He codes in Python, Java, SQL, and Bash, and works extensively with Linux and open-source tools.

Alexandra Alexis

Site Reliability Engineer
Worked with Citi.
With 7+ years of experience building, testing, and maintaining infrastructures and tools to allow for the speedy development and release of the software. Proven expertise in setting up CI/CD for applications, Policy Automation, Jenkins, Web Logic, Java applications, and AWS CodeDeploy.

Jay Medina

Site Reliability Engineer
Experienced in AI integration, infrastructure and IT management.
Experienced IT professional with expertise in strategic planning, project management, and information security. Skilled in developing IT strategies and driving execution for organizations. Strong background in infrastructure setup and systems development.

Roger Jacob

Site Reliability Engineer
Specializes in React and Amazon Web Services infrastructure.
Front-end engineer with 6+ years of experience and specializing in React and Amazon Web Services infrastructure.
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Frequently asked questions

Most teams see matched candidates within 48 hours. Our pre-vetted network means we are not starting from scratch when you reach out. If we do not have the right person available, we will tell you upfront rather than scramble.
Practitioners vet practitioners. Your growth marketer candidate gets interviewed by someone who has run growth at similar companies, not a recruiter checking keywords. We combine technical assessments, portfolio reviews, and cultural fit screening before anyone reaches your inbox.
Contract talent works on defined projects with clear deliverables. Embedded talent integrates into your team for ongoing work, attending standups and using your tools. Both can transition to full-time if the fit is right. We help you choose based on scope, timeline, and budget.
If it is not working in the first few weeks, we will find you a replacement at no additional cost. We are not in the business of defending bad matches. Most clients never need this because we invest heavily in fit upfront.
We offer flexible engagement models: hourly for short-term projects, monthly retainers for ongoing work, or placement fees for full-time hires. No hidden fees, no long-term contracts unless you want them. Your account manager will walk through options on your discovery call.
Yes. Someone who thrived at Google with dedicated QA, DevOps, and product managers may flounder when they have to wear all three hats. We look for people who have operated at your stage, pace, and level of ambiguity.
Yes, and not just the flashy demo-building part. Our Agent Operations practice includes people who have run AI agents in production and dealt with the messy reality of keeping them reliable, cost-effective, and safe.
Most marketplaces optimize for volume. They send you 10 candidates hoping one sticks. We optimize for fit. We would rather say “we do not have the right person” than waste your time. Our 95% satisfaction rate comes from being selective, not fast.