Software Engineer - AI Integration & Data Platforms
$38,400–$58,800 year
On-siteBratislava, Bratislava Region, Slovakia
Job Summary
Design, build, and maintain AI-enabled software solutions, including MCP servers, agent-facing tools, and secure API integrations using Python and modern backend frameworks. Implement server-side capabilities that expose business functions and data access patterns to AI agents while enforcing security guardrails. Develop and enhance CI/CD pipelines in Azure DevOps to support automated builds, testing, and repeatable release processes. Deploy cloud-native services using Azure-based patterns, containerized workloads, and scalable runtime architectures. Partner with product, engineering, and security stakeholders to translate requirements into durable technical solutions and document APIs and deployment patterns. Lead moderate-sized technical efforts and contribute to architecture decisions to accelerate automation and AI adoption.
Required Qualifications
- Python development for applications, services, automation, or integration work
- Proficiency with general software engineering languages and practices, with the ability to work across modern programming stacks as needed
- API development using modern backend frameworks and secure service design patterns
- MCP server or AI tool development, including exposing structured tools, data access, or workflow automation to AI agents
- Azure DevOps, Git-based development workflows, CI/CD pipelines, automated testing, and release automation
- Strong understanding of software engineering fundamentals, including code quality, modular design, version control, security, logging, monitoring, and documentation best practices
- Experience collaborating across engineering, data, AI, security, and business teams to gather requirements and deliver reliable enterprise software solutions
Desired Qualifications
- Experience with FastAPI, FastMCP, SQLAlchemy, containerized deployments, or cloud-hosted Python services
- Familiarity with Azure services, managed identity, Key Vault, container platforms (Docker), private networking, or enterprise authentication patterns
- Experience with AI-assisted development tools, prompt-aware engineering workflows, agentic architectures, or LLM application integration
- Hands-on experience with LangGraph and LangChain for developing LLM-powered applications, agents, and workflow orchestration
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