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Alexandra Alexis

ChromaDB 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.

Nikhil Sharma

ChromaDB Engineer
Proficient in Kubernetes, Terraform, LLMs, and LangChain Run.
Full-stack Flutter developer with 5 years of Flutter and 6 years of programming experience, building web and mobile applications

Judson Terrell

ChromaDB Engineer
Worked at Cadient and Cognizant.
Judson Terrell is a web application and ionic developer expert specializing in User Interface Design and server-side and database development in various languages. During his startup, he developed applications for the capital markets division with Wells Fargo. Stakeholders used the applications to evaluate data within the company for the quarterly report. Judson is skilled in Angular, UI Design, Full Stack Development, and JavaScript.

Patrick Casey

ChromaDB Engineer
Former Senior Software Developer at Shopify.
Patrick Casey is a technical leader and full-stack engineer with 9 years of hands-on experience creating microservices, APIs and web applications. He possesses a broad knowledge of web and Microsoft technology stack, including PHP, JS frameworks, .NET. While he is working in several companies as a core engineer, he has learned how to deliver critical and highly available software systems, and secure, and lead technical teams, by creating an easy development environment, fixing technical debts, providing the best codes and mentoring dev. Patrick was also a senior software developer at Shopify, where he developed a Django app integrated with the Jira platform and a slack bot for knowledge discovery.

Richard Ball

ChromaDB Engineer
Principal data scientist at Kinetic.
Experience leading the machine learning competency, which involves scoping machine learning problems and driving project adoption, deploying and managing fraud detection and NLP models in our production machine learning infrastructure on AWS, and building a new infrastructure for production machine learning using Spark and MLflow.
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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.
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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.