A growing EU-based AI data analytics startup reached out to us with a clear challenge: they got their first sale and needed to scale the product immediately, but without a recruiter in-house and pre-vetted developers ready to go, it was slightly challenging to hire top-tier experts fast. And considering that it’s an SME without funding, it was essential to save hiring costs and cut operational expenses, so staff augmentation looked like a perfect solution.
Their key pain points:
- Time-to-market was slowing down due to limited backend and DevOps support
- Their CI/CD pipeline was slow and often unreliable
- They had feature ideas but lacked the frontend capacity to build them
- Infrastructure was mostly manual and hard to scale
They used a modern stack — Python for backend and ML components, React for frontend, and AWS for hosting — but lacked the hands to maintain, improve, and scale it.
They wanted 2 Senior Developers who could:
- Act as embedded team members, proactive guys with strong soft skills and cultural fit
- Own features end-to-end
- Help clean up and stabilize the infrastructure
- Suggest improvements, not just follow orders, think outside the box
Developers with the same business mindset — those who understand things without needing many words
They had tried some freelancers before, but what they needed was committed, senior-level support. We sourced quite a few candidates, and from our side one(!) Senior Python/React with DevOps skills, who has the necessary qualifications to manage their requests and who could combine expertise of 2 separate experts.
Data Analytics AI startup
Python, React, Kubernetes, ElasticSearch, PostgreSQL
EU
18 April 2025
Once our developer were onboarded into their Slack, GitHub, and sprint calls, he quickly identified weak points in the system. Within the first month, he tackled both short-term blockers and longer-term improvements.
Highlights:
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Refactored core Python APIs, improving performance and reducing bugs
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Increased test coverage from 25% to over 70%, reducing QA time
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Migrated key frontend views to optimized React components
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Reduced frontend bundle size by ~25%, speeding up load time
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Introduced Terraform for infrastructure management (previously manual)
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Rebuilt CI/CD pipeline — deployments now take 3 minutes instead of 8
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Added monitoring via Sentry and CloudWatch to catch and act on errors quickly
Our engineer participated in planning, demos, and even helped onboard a new internal dev.
- All features were delivered in time.
- The development cost has been reduced by 23% compared to the expected amount.
- All key functionality was covered by tests, which reduced QA costs by 10%.
This project is a great example of how an AI product can scale quickly with less effort and save costs by hiring the right pre-vetted, ready-to-go talent provided by a reliable external partner. We staffed a Senior Engineer who didn't just code - he brought real product value from day one. His ability to plug into an early-stage, fast-moving environment was a key reason the client kept expanding the relationship.
Through strong communication, high-quality delivery, and deep expertise across Python, React, and AWS, our engineer became a trusted member of the team.
The key to success here was "to be on a same page with the customer and talk on a same language with the team". We focused on the developer who had:
Startup experience
Full-stack expertise
DevOps skills
Proactive problem-solving business mindset
C1 level of English
Summary of key outcomes:
Successfully integrated Senior Python/React Developer with DevOps skills within 1 week
Cut deployment time by more than half
Brought test coverage to enterprise standards
Reduced frontend weight and improved load speed
Enabled faster iteration cycles across product and AI features
Created infrastructure that's now easier to scale and maintain