Prague: Senior AI-Assisted Software Engineer (m/w/d) Only employees / no freelancers possible

Job Description

Senior AI-Assisted Software Engineer (m/w/d) Start: asap End: 31.12.2026 + Possible extension in 2027 Utilization: 100% (40 hours per week) Location: Mostly remote, sometimes on-site required - ocassionally for a workshop, estimated 1-2x per month (Prague) Language: English (min B2, better C1) Important: We can only present employees. No freelancers possible. We care less about how many technologies you can list and more about how you think, how quickly you learn, how effectively you use AI to extend your capabilities, and whether you can own a problem from the first conversation to a reliable solution in production. Hands-on Senior AI-Assisted Software Engineer with a Forward Deployed Engineer mindset, with ability and capability to analyze an ambiguous business or technical problem, identify what matters, and turn it into a reliable production solution. Supporting enterprise digital platforms for pricing, tender management, workflow automation, and system integration. The role spans the full delivery lifecycle—from discovery and prototyping through architecture, implementation, testing, deployment, troubleshooting, and operational handover. This is not a narrow backend, frontend, DevOps, or AI specialist role. Needed is a strong software engineering foundation, broad delivery experience, and the ability to learn unfamiliar technologies quickly. Practical use of GitHub Copilot and other approved AI capabilities should be a natural part of the work, but all outputs remain subject to engineering judgment, validation, security controls, testing, and human accountability. Task Description Own problems end to end: work with Product Owners, users, architects, engineers, testers, and operations to turn incomplete requirements into testable workflows, explicit decisions, and production outcomes. Deliver working software early: build prototypes and production increments, validate them with users, and refine scope through evidence and short feedback cycles. Engineer across boundaries: design and implement backend services, web applications, APIs, integrations, data flows, supporting automation, and cloud infrastructure. Build for production: address scalability, resilience, security, observability, supportability, cost, deployment, rollback, and operational ownership from the start. Use AI deliberately: apply GitHub Copilot and other approved tools to understand codebases, explore options, implement bounded changes, generate and improve tests, troubleshoot incidents, support migrations, review code, and maintain documentation. Control AI-assisted work: break work into reviewable steps; validate, test, and refactor generated outputs; detect hallucinated APIs, incomplete changes, insecure code, unnecessary dependencies, and misleading tests. Choose the right approach: use deterministic automation for predictable rules and repeatable execution, AI for interpretation or generation, and hybrid designs when flexibility must be combined with control and auditability. Establish quality: define practical gates for build, testing, security, architecture, documentation, and release readiness. Maintain traceability between outcomes, requirements, decisions, implementation, tests, and release evidence. Lead hands-on: design, code, test, review, troubleshoot, mentor, facilitate architecture decisions, communicate trade-offs, and leave product teams able to own and evolve the solution. Requirements Strong hands-on experience delivering production software in an enterprise environment. Ability to work across backend, frontend, APIs, integrations, cloud infrastructure, CI/CD, testing, and production operations. Evidence of taking an unclear request through discovery, architecture, implementation, deployment, and support. Sound judgment in software design, modularity, security, performance, maintainability, and operational readiness. Experience with distributed systems, enterprise integrations, shared delivery dependencies, and unfamiliar or insufficiently documented codebases. Strong debugging and root-cause analysis skills across application, integration, infrastructure, and data boundaries. Experience making and documenting technical decisions under incomplete information. Clear written and verbal communication in English, including the ability to explain risks and trade-offs without unnecessary jargon. Technology Landscape Projects may use Java, Spring Boot, Kotlin, Python, Django or FastAPI, React, TypeScript, Microsoft Azure, Kubernetes or OpenShift, Docker, GitHub Actions, Oracle, PostgreSQL, MongoDB, Kafka, Azure Service Bus, API gateways, Splunk, and Grafana. Expertise in every technology is not required;