Edvantis: 17 open roles — Lviv, Ukraine; Rzeszow, Poland; Berlin, Germany; New York, United States

About Edvantis

Edvantis provides software engineering, data and AI, cloud and quality assurance services. Its official careers board publishes full role descriptions and source country and workplace labels for opportunities across its delivery locations.

People & hiring

Company scale, with the denominator visible

Employee figures and vacancy signals describe different things. Each chart names its scope and observation period.

No sourced employee headcount or technical-staff share is available for this company yet. Vacancy counts below are never used as employee estimates.

ApplyDjinn hiring evidence · current 30-day window

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Current open roles

17

History is collecting

Daily history is collecting.

Technical open roles

1694%

Classified from the same current vacancies. This is a hiring mix, not the employee mix.

17

Current openings · ≤30 days

—

Founded

—

Rating

Live vacancy evidence

Technology coverage at Edvantis

Coverage always uses all 17 active roles as the denominator. Scope only changes which requirement signals count.

Active roles: 17
Technologies: 40
Hard mentions: 170

Automation

12 of 17 roles

71%

Core: 33%
Required: 33%
Context: 33%
Nice: 0%

AI

8 of 17 roles

47%

Core: 50%
Required: 50%
Context: 0%
Nice: 0%

AWS

10 of 17 roles

59%

Core: 10%
Required: 80%
Context: 0%
Nice: 10%

CI/CD

11 of 17 roles

65%

Core: 0%
Required: 82%
Context: 0%
Nice: 18%

Databases

10 of 17 roles

59%

Core: 0%
Required: 90%
Context: 10%
Nice: 0%

SQL

9 of 17 roles

53%

Core: 0%
Required: 100%
Context: 0%
Nice: 0%

Cloud

13 of 17 roles

76%

Core: 0%
Required: 62%
Context: 31%
Nice: 8%

Python

5 of 17 roles

29%

Core: 60%
Required: 40%
Context: 0%
Nice: 0%

JavaScript

6 of 17 roles

35%

Core: 17%
Required: 83%
Context: 0%
Nice: 0%

GenAI

4 of 17 roles

24%

Core: 75%
Required: 25%
Context: 0%
Nice: 0%

Java

4 of 17 roles

24%

Core: 50%
Required: 50%
Context: 0%
Nice: 0%

React.js

6 of 17 roles

35%

Core: 0%
Required: 83%
Context: 0%
Nice: 17%

REST

6 of 17 roles

35%

Core: 0%
Required: 83%
Context: 17%
Nice: 0%

Docker

5 of 17 roles

29%

Core: 0%
Required: 100%
Context: 0%
Nice: 0%

Full Stack

5 of 17 roles

29%

Core: 40%
Required: 20%
Context: 40%
Nice: 0%

Backend

7 of 17 roles

41%

Core: 0%
Required: 57%
Context: 43%
Nice: 0%

Azure

4 of 17 roles

24%

Core: 0%
Required: 100%
Context: 0%
Nice: 0%

Git

4 of 17 roles

24%

Core: 0%
Required: 100%
Context: 0%
Nice: 0%

Kubernetes

4 of 17 roles

24%

Core: 0%
Required: 100%
Context: 0%
Nice: 0%

RAG

4 of 17 roles

24%

Core: 0%
Required: 100%
Context: 0%
Nice: 0%

TypeScript

4 of 17 roles

24%

Core: 0%
Required: 100%
Context: 0%
Nice: 0%

LLM

3 of 17 roles

18%

Core: 33%
Required: 67%
Context: 0%
Nice: 0%

Node.js

3 of 17 roles

18%

Core: 33%
Required: 67%
Context: 0%
Nice: 0%

DevOps

6 of 17 roles

35%

Core: 0%
Required: 50%
Context: 50%
Nice: 0%

Microservices

5 of 17 roles

29%

Core: 0%
Required: 60%
Context: 40%
Nice: 0%

Software Architecture

4 of 17 roles

24%

Core: 0%
Required: 75%
Context: 25%
Nice: 0%

LangGraph

3 of 17 roles

18%

Core: 0%
Required: 100%
Context: 0%
Nice: 0%

Linux

3 of 17 roles

18%

Core: 0%
Required: 100%
Context: 0%
Nice: 0%

Playwright

3 of 17 roles

18%

Core: 0%
Required: 100%
Context: 0%
Nice: 0%

PostgreSQL

3 of 17 roles

18%

Core: 0%
Required: 100%
Context: 0%
Nice: 0%

Scrum

3 of 17 roles

18%

Core: 0%
Required: 100%
Context: 0%
Nice: 0%

Terraform

3 of 17 roles

18%

Core: 0%
Required: 100%
Context: 0%
Nice: 0%

GitHub Actions

3 of 17 roles

18%

Core: 0%
Required: 67%
Context: 0%
Nice: 33%

Jira

3 of 17 roles

18%

Core: 0%
Required: 67%
Context: 0%
Nice: 33%

Machine Learning

3 of 17 roles

18%

Core: 0%
Required: 67%
Context: 33%
Nice: 0%

Algorithms

2 of 17 roles

12%

Core: 0%
Required: 100%
Context: 0%
Nice: 0%

API

2 of 17 roles

12%

Core: 0%
Required: 100%
Context: 0%
Nice: 0%

Confluence

2 of 17 roles

12%

Core: 0%
Required: 100%
Context: 0%
Nice: 0%

Databricks

2 of 17 roles

12%

Core: 0%
Required: 100%
Context: 0%
Nice: 0%

FastAPI

2 of 17 roles

12%

Core: 0%
Required: 100%
Context: 0%
Nice: 0%

Company Details

Industry

Software Engineering / Data and AI Services

Company size

—

Headquarters

Lviv, Ukraine; Rzeszow, Poland; Berlin, Germany; New York, United States

Founded

—

Domain

edvantis.com

Rating

—

Open roles

17

Technical roles

16 of 17

Observed hiring change

1 observation

Hiring and market history

Daily vacancy snapshots show this company’s observed hiring; no standalone public ticker is assigned.

Open roles

Vacancy history

17 observed on 2026-10-15
Trend appears after another daily observation.

Current role mix

16 technical · 1 other

94% of currently observed roles are technical.

Employee perspectives

Reviews of Edvantis

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Company monitor

Published about Edvantis

Official updates and independent stories that name Edvantis, with dates and source attribution. Each preview opens here first.

5 verified updatesOfficial feed monitored

View news and attention signals for Edvantis →

Official · edvantis.comOct 9, 2026

5 Legacy System Modernization Strategies That Bring ROI

Legacy software remains the engine under many companies’ hoods, but that decision comes at a premium. About three-quarters have 70% of their IT spending locked in supporting aging software. Naturally, this restricts new development initiatives as resources are tied up in maintenance, which is a feat of its own. Even a small product update requires extensive testing across tightly connected legacy components. Yet replacing the system carries its own risks. Years of embedded business logic make a full rewrite expensive, especially when daily critical operations depend on it. So meddling with this could pose risks to business continuity. That said, legacy system modernization doesn’t have to be an all-at-once approach. You can modernize incrementally, tackling the components causing the most friction while limiting disruption to daily operations.

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Official · edvantis.comSep 7, 2026

7 Microservices Architecture Patterns for 2026

Microservices have been a mainstream application architecture pattern for more than a decade. The core idea of splitting services into smaller components still holds, but new challenges have emerged. Service-to-service communication, distributed data management, security controls, and infrastructure overhead are among the top concerns for larger microservices estates. At the same time, engineering teams are under pressure to control cloud costs and further evolve the system design for AI-enabled applications. These factors are pushing companies towards a more selective approach to microservices architecture design. Some are consolidating services into modular monoliths. Others are introducing event-driven communication, serverless components, or stronger patterns for distributed transactions and service security. To get you up to date, Edvantis cloud engineering team prepared a refresher on microservices architecture patterns – their advantages and implementation tradeoffs.

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Official · edvantis.comAug 13, 2026

9 Real Estate Software Integrations That Elevate CX

Real estate markets are beginning to regain momentum after several subdued years. Transaction volumes and prices are gradually improving despite persistently high mortgage rates. Yet recovery does not make client acquisition easier. Buyers and sellers have become more discerning with their choice of an agent, and they’re often swayed by the customer experience (CX) the company offers. Service quality then depends heavily on how consistently the agent communicates and responds throughout a long decision cycle. Expectations are already visible: 35% of clients said agents could improve their communication, even though 45% identified responsiveness as a positive part of their experience. Personalization carries just about the same weight. Around 74% of customers expect real estate businesses to tailor their experience, while 69% are willing to pay more when that experience meets a higher standard.

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Official · edvantis.comJul 20, 2026

The API Economy Trends: 5 Fastest Growing Sectors for This Decade

Many companies own assets that other businesses are willing to pay for: proprietary data, domain logic, workflow automation, infrastructure access, or software capabilities built for internal use. APIs make those assets programmable and securely distributable. For some, APIs create a new provider-side revenue stream by turning existing capabilities into products. For others, API consumption changes the economics of product development by making it possible to add payments, location intelligence, AI, network services, or cloud controls without custom-coding each capability. Both sides form the booming API economy — a $23.8 billion market today and a $128.9 billion one by 2036, where new demand is forming quickly. - Understanding the API economy - 5 Fastest-Growing Sectors of The API EconomyNetwork APIsAgentic Commerce APIsAI Model APIsCloud Orchestration and Optimization APIsFinancial APIs - Conclusion

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Official · edvantis.comJun 22, 2026

How We Migrated 300+ Databases from On-Prem SQL Server to Azure SQL Managed Instance

Over 300 SQL Server databases. Each with its own SSIS package — some running PowerShell, some wrapping VBScript, some calling .NET apps on local Windows servers. A financial data platform that had grown for years, serving data to consumers who needed it to be right, every day, on time. By the time we got involved, the platform was carrying years of accumulated technical debt. It had grown faster than anyone could refactor it, and there was more to fix than there was to build. On paper this was a SQL Server to Azure SQL migration. In practice, it became a full rebuild. What started as a move to Azure SQL Managed Instance — 300+ databases lifted off on-prem hardware — turned into a full re-architecture across three layers: scraper, pipeline, and database. Critical incidents dropped by 70–85%. End-to-end latency went from up to half an hour to under five minutes. And 300+ unique SSIS packages became one ADF pipeline. The problem was never where the system ran. It was how it was built. This article is about how we got there — what we delivered, what broke, and what we learned. - The System We Inherited - Why We Chose to Re-Architect, Not Migrate - Building the Three LayersThe Scraper LayerThe ADF Pipeline LayerThe Database Layer - What Broke Along the Way - Making Git the Source of Truth - SQL Server to Azure SQL Migration: Results Before and After - Conclusions - Takeaways - And one more thing… about AI

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