41 Devoteam Jobs Hiring Near You

• Design and lead the strategy for cloud‑native application architectures, ensuring high availability, scalability, and resilience across hybrid or multi‑cloud environments.
• Bridge the gap between development and operations by championing DevOps and DevSecOps cultures, automating CI/CD pipelines, and integrating security into the build process.
• Drive modernization initiatives, migrating legacy monolithic systems to microservices, serverless, or containerized architectures using cloud‑native design patterns.
• Leverage AI‑assisted software engineering tools (e.g., GitHub Copilot, ChatGPT Enterprise) to optimize the SDLC, enhancing developer productivity, code quality, and reliability.
• Collaborate with stakeholders to translate business goals into technical roadmaps, selecting the right mix of cloud providers (AWS, Azure, GCP) and platforms (Red Hat OpenShift, Kubernetes).
• Provide hands‑on technical leadership to development teams, conducting code reviews and ensuring best practices in software development (Java, C#, Python, or Node.js).
• Define and enforce standards for infrastructure as code (IaC), automated testing, and release management to accelerate time‑to‑market.
• Mentor senior developers and engineers, fostering a culture of continuous improvement and technical excellence.

• Strong analytical and problem‑solving skills, with the ability to navigate complex architectural challenges in distributed systems.
• Proven experience in leading technical teams and influencing decision‑making at a strategic level.
• Strong communication skills, capable of explaining complex cloud‑native concepts to non‑technical stakeholders and C‑level executives.
• Deep understanding of software development methodologies, particularly Agile and Scrum, with a focus on iterative delivery.
• Willingness to keep up to date on the rapidly evolving Cloud Native Computing Foundation (CNCF) landscape, following training and obtaining relevant certifications (e.g., CKA, AWS Solutions Architect).
• Fluency in Dutch or French and good command of English.
• Strong software development background with mastery of at least one major language such as Java, C#, Python, or Node.js, and a solid understanding of the others.
• Expert knowledge of Cloud‑Native architectures, including Microservices, Event‑Driven Architecture, and Serverless patterns.
• Proven experience with application modernization strategies, effectively refactoring or re‑platforming legacy applications into cloud‑native ecosystems (Strangler Fig pattern, Anti‑Corruption Layer, etc.).
• Demonstrated experience implementing AI‑assisted engineering, utilizing Generative AI tools to automate boilerplate code, generate unit tests, and improve documentation reliability.
• Extensive experience with Containerization and Orchestration, specifically Docker, Kubernetes, and enterprise platforms like Red Hat OpenShift.
• Proficiency in one major cloud platform (AWS, Azure, or GCP) with the ability to design cloud‑agnostic or hybrid solutions.
• Strong understanding of DevOps and DevSecOps principles, with hands‑on experience setting up CI/CD pipelines (Jenkins, GitLab CI, Azure DevOps) and Test Automation.
• Experience with Infrastructure as Code (IaC) tools such as Terraform, Ansible, or CloudFormation.
• Experience or certifications in Architecture Framework or Modelling (TOGAF, C4, ArchiMate, …) are considered as assets.

• Work as part of the Data & AI Factory team, supporting the customer in data‑ops and AI projects.
• Design, build, maintain and troubleshoot data pipelines, models and transformations.
• Help build, maintain and troubleshoot data observability solutions to improve data quality.
• Turn data into valuable business insights, preparing custom solutions optimised for each client.
• Act as a trusted advisor for junior team members and lead by example.

• Mid‑level engineer who enjoys breadth and is comfortable writing Python backends.
• Curious about AI, not afraid of data pipelines, communicates well, and gets things done.
• Exposure to data engineering concepts, pipelines, SQL, and data transformation.
• Curiosity about AI and agentic systems; hands‑on experience is a plus, not a must.
• Familiarity with at least one public cloud environment (Azure preferred).
• Good communication skills in English and Lithuanian.
• Growth mindset – motivated to develop across backend, AI, and data.
• Nice to have: experience with data tools like Databricks or similar, familiarity with AI development tools (LLM APIs, MCP, Claude Code, or similar).

• Bridge the gap between technical development and business requirements.
• Own the end‑to‑end testing lifecycle—from defining acceptance criteria to managing defect resolution—in an Agile environment.
• Develop, document, and operationalise comprehensive test acceptance criteria (UAT) to ensure all features align with business goals.
• Methodically confirm the thorough testing of all customer requirements, covering functional and non‑functional specifications.
• Oversee the lifecycle of errors and bugs, ensuring they are identified, logged, prioritised and resolved.
• Utilise Agile and Scrum methodologies to streamline testing workflows and improve speed‑to‑market without compromising quality.

• Provide support to stakeholders filling risk cards.
• Review and challenge content of risk cards to ensure quality.
• Design and continuously improve action plan follow‑up.
• Ensure actions plans are formalised with all needed information.
• Follow‑up and report on risk register entries and action plans to provide an accurate and up‑to‑date risk view.
• Assist in reporting and preparation of regular IT risk and control committees.

Your expertise in data modelling tools and techniques, as well as your ability to communicate complex data concepts to non‑technical stakeholders, will be critical to the success of our data‑driven projects.

Maintain documentation and metadata.

• Design and implement the data governance framework, operating model, and roles and responsibilities.
• Develop and implement data governance process workflows.
• Conduct requirement gathering and business analysis related to data governance initiatives.
• Support data architecture activities in coordination with data governance objectives.
• Participate in data maturity assessment sessions, gap analysis, and alignment with Enterprise Architecture teams and data strategy.
• Coordinate with project teams to ensure that data standards and models align with enterprise architecture and applicable standards.
• Define data governance‑related roles and responsibilities, processes, and procedures.
• Establish a common understanding and visibility of all organisational data assets.
• Build strong working relationships with business stakeholders and provide business‑impact‑driven analysis, communicating results effectively.