01
Requirement Gathering
We start by understanding your AI project's nuances, desired skill sets, and any specific domain expertise you may require.
Team extension
AI Developers for your Team Extension
Let us take the guesswork out of recruiting and streamline your hiring process with our data-driven recruitment services. Check also our AI Developers recruitment services.
Teams extension
Staff augmentation and recruitment models
Pay for time worked with no hidden commissions — or full and part-time recruitment with retention built in.
Staff augmentation
In this model, the client pays for the time actually worked by the employee — there are no hidden costs or commissions.
Full and part-time recruitment
We specialize in data-driven recruitment processes, with favorable pricing, improved time-to-hire, and a retention program to ensure business continuity.
AI Developers Recruitment
From requirements to onboarded specialists
Five steps, from gathering your requirements to keeping the right people on the team after they join.
Discuss hiring needs 02
Talent Sourcing
Leveraging our extensive network, we identify potential candidates who fit the bill. This includes scrutinizing technical acumen, past project experience, and domain-specific knowledge.
03
Vetting and Validation
Before introducing candidates, they undergo rigorous vetting — technical assessments, interviews, and soft-skill evaluations so they align with your culture.
04
Final Selection
We present a curated list of the best-fit AI developers for your project. The decision to proceed remains entirely in your hands.
05
Ongoing Support and Retention
Our relationship does not end with recruitment. We offer continued support, smooth onboarding, and addressing subsequent concerns.
Roles
Key roles we recruit for
Machine Learning Engineers
The role of Machine Learning Engineers has become increasingly pivotal in the modern business landscape. These professionals specialize in developing algorithms and models that enable machines to learn and make decisions with minimal human intervention. Their expertise is crucial across sectors from finance to healthcare — developing machine learning models, data analysis and interpretation, and automating data-driven processes.
Data Scientists
Data Science Specialists extract valuable insights from vast datasets. Their expertise in data mining, interpretation, and analysis informs business strategies and operational improvements through statistical analysis, predictive modeling, machine learning implementation, and data visualization.
LLM and NLP Engineers
LLM Engineers build and refine large language models that process vast amounts of text — chatbots, virtual assistants, and AI-driven applications using technologies like BERT and GPT. NLP Engineers develop algorithms that enable machines to understand and respond to human language for sentiment analysis, text classification, and language translation.
Programmers with AI
Python and Java programmers with experience in generative AI and frameworks such as LangChain for specific business needs. They write and optimize code for machine learning models, neural networks, and NLP systems — integrating AI into software products with both theoretical depth and practical skills.
Big Data Engineers
Big Data Engineers create and manage an organization's big data infrastructure — designing, building, and integrating large-scale data processing systems with Hadoop, Spark, Python or Java, and strong analytical skills to convert complex data into actionable insights.
Python Developers
We search for Python talent across backend developers (server-side logic and database management), data scientists (Pandas, NumPy), machine learning engineers (TensorFlow, PyTorch), automation engineers (Selenium), and API developers.
Java Developers
Java expertise across Java web developers (Spring Boot, Jakarta EE), EJB developers, microservices developers (Spring Cloud, Micronaut), middleware developers (Kafka, RabbitMQ), JavaFX developers, and performance engineers.
Azure Developers
Azure solutions architects, DevOps engineers, application developers (App Service, AKS), data engineers (SQL Database, Cosmos DB, Data Lake), AI engineers (Cognitive Services, Azure ML), security engineers, and networking specialists.
React Developers
React frontend developers, React Native developers, full-stack React developers (Node.js), React Redux developers, and Next.js developers for server-rendered applications.
AWS Engineers
AWS solutions architects, DevOps engineers (CodeBuild, CodePipeline), SysOps administrators, security specialists (IAM, Shield), big data engineers (Redshift, Kinesis, EMR), machine learning specialists (SageMaker, Comprehend), and networking specialists (VPC, Direct Connect, Route 53).
Automation Testers
Selenium testers, API automation testers (Postman, RestAssured), mobile automation testers (Appium, Espresso), performance testers (JMeter, LoadRunner), CI specialists (Jenkins, Bamboo), framework architects, BDD specialists (Cucumber, SpecFlow), and test data management experts.
Recruitment cycle
How the recruitment cycle looks like?
Discovery and contract, then data-driven sourcing through onboarding and retention.
Discovery and analysis
Briefing, initial meeting, and price estimation with the offer — aligning on needs and expectations before sourcing begins.
- Briefing
- Initial meeting
- Price estimation with offer
Strategy and contract
Choosing the best team extension model, recruitment strategy, contract signing, and NDA — so everyone agrees on approach before candidates are contacted.
- Team extension model selection
- Recruitment strategy
- Contract signing and NDA
Data-driven recruitment through onboarding
Publishing and promoting job offers, outreach, database matching, proposing candidatures, interviews, offer, onboarding, retention, and risk management for business continuity.
- Sourcing and skills matching
- Interviews and offer
- Onboarding and retention
Estimate cost of your team
Get a quote for staff augmentation or full recruitment — roles, seniority, and timeline scoped in one briefing.
Offer advantages
Data-driven hiring with zero recruitment fee
Retention, continuity and a 20-day average time-to-hire.
Data-driven recruitment process
Improving hire quality has a direct impact on work efficiency. We score profiles against project and customer requirements to make data-driven decisions instead of mood-driven ones — avoiding high-cost mistakes.
Retention program
Community membership and structured check-ins keep specialists engaged after onboarding — the program behind our published retention rate.
Zero cost of recruitment
The customer's cost-per-hire for specialist pre-vetting is zero on our side — hiring managers spend less time screening and more time on core delivery.
Business continuity
HR monitors performance monthly; quarterly deep reviews with your team catch fit issues early. If something goes wrong, recovery and backfill plans activate before delivery slips.
Time-to-hire
Vstorm's average time to hire is 20 days — hired employees add value faster than the industry average IT hiring cycle.
Proof
Team extension success stories
Sonorys, Viessmann, and Atos — remote engineers and testers embedded through Vstorm recruitment.
Vstorm and Sonorys — A Collaborative Triumph in Telecommunications
Through a rigorous selection process, Vstorm successfully identified and delivered highly qualified engineers to Sonorys. These engineers seamlessly integrated into Sonorys' R&D team, working remotely and contributing their expertise to the company's projects.
Remote Testing Resources for Viessmann R&D Center
Viessmann's main goal was to hire remote testers to augment their in-house R&D quality assurance team.
Remote extended tech teams for Atos
The primary objective was to augment the international IT teams in Atos with highly qualified, experienced IT specialists.
Other services
Generative AI Lab and product builder
Get to know what we write about
Strategy
Instant customer service. AI chatbots in e-commerce
AI chatbots in e-commerce, in particular, have become integral in enhancing customer service, providing instant, personalized responses to customer inquiries, and significantly improving user experience.
Agentic AI
Lesson 3: System access is a hard constraint
Why system access (VPNs, API keys, ERP exports) belongs on the critical path of an agentic AI project, with a named owner and deadline.
Agentic AI
Lesson 2: Why data without context is worthless
Database records capture outcomes, not the reasoning behind them. Why AI projects need context capture and data experts from the architecture stage.
Scale your squad
Add senior AI talent without the recruiting overhead.
Define roles, review a vetted shortlist, and onboard specialists who integrate with how your team already works.