Role Description Support technical due diligence and transition readiness by using AI-assisted software engineering methods to understand complex codebases, reconstruct dependencies, diagnose builds, accelerate documentation, and improve handover readiness in enterprise cloud and platform environments. A core objective of the role is to help analyse and evolve a cloud software stack derived from OpenStack, including its service decomposition, control-plane components, interfaces, and build structure. This position targets repository-scale code understanding, dependency discovery, build and compile diagnosis, technical documentation generation, and AI-assisted support for software handover activities in an enterprise setting. The candidate should be comfortable working with a modular cloud platform architecture based on OpenStack-style services and surrounding infrastructure components. Key Responsibilities - Analyse large software repositories to map services, dependencies, interfaces, build flows, and technical risks relevant to technical due diligence. - Reconstruct the architecture of a cloud software stack derived from OpenStack, including key service boundaries, integration points, APIs, and operational dependencies. - Use AI coding agents and structured prompting patterns to accelerate code understanding, reverse engineering, and documentation generation across large codebases. - Support build-log diagnosis, compile issue triage, and reproducibility analysis across CI/CD pipelines and container-based delivery environments. - Produce structured handover artefacts including architecture summaries, dependency maps, code quality observations, and readiness assessments for receiving engineering teams. - Work closely with software architecture, DevOps, testing, and security specialists to convert AI-assisted insights into actionable engineering outputs. Examples of Market Tools and Models Expected - AI coding environments such as Windsurf, Cursor, Claude Code, or VS Code-based AI extensions for large-repository engineering workflows. - Frontier coding models such as Anthropic Claude Opus-class models and newer, high-capability coding models used through enterprise-approved interfaces or bring-your-own-key setups. - Supporting engineering tools such as GitHub Enterprise, GitLab, Jenkins, ArgoCD, Helm, Docker, Kubernetes, and terminal-native automation workflows. - Familiarity with OpenStack-oriented cloud software environments and adjacent components such as Nova, Neutron, Cinder, Keystone, Glance, and Kubernetes integration patterns is highly valuable. Qualifications - 5+ years in software engineering, platform engineering, or DevOps-oriented development roles, with recent hands-on work on LLM-enabled engineering workflows preferred. - Strong coding skills in Python plus experience in at least one systems or backend language such as Java, Go, C, C++, or Rust. - Solid understanding of Git, CI/CD, container builds, Kubernetes-based delivery, and software architecture analysis. - Experience with codebase exploration, reverse engineering, technical debt identification, and engineering documentation in complex environments. - Good working knowledge of cloud platform architectures derived from OpenStack, including modular service design and multi-component integration patterns, is strongly preferred. - Comfortable working in high-accountability enterprise settings with strong expectations on confidentiality, evidence quality, and structured deliverables. Additional Information - You will be working in the European Union to meet our customers' data security and privacy requirements. - Please be informed that our remote working possibility is only available within Hungary due to European taxation regulation.

Role Description GTM engineering is what happens when you replace headcount with systems. Instead of hiring ten SDRs, you build automated pipelines that research accounts, enrich leads, personalize outreach, and route signals to the right rep at the right moment. AI made this possible. Very few people know how to do it well yet. You could become one of them. You will work at the intersection of sales, marketing, and engineering: - Building automation workflows with AI tools (Claude, GPT, agent frameworks, Make, our own AI Crew platform) - Wiring up data pipelines between our CRM, enrichment tools, and outbound systems - Shipping experiments that directly move pipeline We use our own product for this, which means you will build AI agents on the same platform we sell. Your work will not sit in a slide deck. It will run in production and generate revenue. Goals - Build and maintain automated prospecting workflows: account research, lead enrichment, signal detection, outreach personalization - Create custom AI agents on Demodesk's AI Crew platform and connect them to our GTM stack - Wire up integrations between our CRM (HubSpot), enrichment tools, and outbound platforms using APIs, Make, and scripts - Run growth experiments end to end: hypothesis, build, launch, measure, iterate - Automate internal GTM reporting so the team spends time on decisions, not spreadsheets - Document what works so your systems outlive your internship Qualifications - You are AI native: you already use LLMs daily to build things, not just to chat. - Basic technical skills: you can write scripts (Python or JavaScript), work with APIs, and are unafraid of JSON. - A builder mindset: you prototype first and ask for permission later. - Interest in how B2B companies acquire customers: sales, marketing, growth. - Currently enrolled in or recently graduated from a Bachelor's or Master's program, or equivalent practical experience. - Fluent English; German is a plus. Nice to have - Experience with automation platforms (Make, Zapier, n8n) or CRM systems. - A side project, GitHub repo, or automation you built that you are proud of (show us!). - First exposure to outbound sales tooling or growth work. Benefits - Well-funded and backed by Silicon Valley’s top investors & angels, including Y Combinator. - Full responsibility from day one and being part of our hierarchy-free and results-driven working environment. - Paid internship with a competitive monthly salary. - Real ownership: your systems run in production from week one. - Direct mentorship from our GTM and engineering leadership. - The full AI toolkit: we pay for the best AI tools available, and you will use them all day. - Top-of-line equipment. - Team face time in Munich and a front-row seat at an AI company defining a new category. - A strong pipeline to a full-time role: interns who ship stay. Company Description Demodesk is the AI that does the job, not the AI that tells you what to do. For ten years, sales AI has handed dashboards to overwhelmed reps and called it transformation. We built something different: AI agents that join every sales conversation, update the CRM, send the follow-ups, coach reps after every call, and flag deals at risk before they slip. Founded in Munich in 2018 and backed by $15M from Balderton, Y Combinator, and the founders of HubSpot and Pipedrive, we serve 300+ customers and 20,000+ sales professionals across Europe, including Porsche, Bosch, Treatwell, OMR, and Enpal. GDPR-native, ISO 27001 certified, EU-only data. We are still small enough that everyone's fingerprints are on the product.

• Translate customer business goals into platform value narratives, architecture principles, and adoption and expansion logic • Co-create enterprise architectures with customers, spanning cloud, edge, and operational environments • Design transformation roadmaps with a phased adoption approach and value realization plan • Identify expansion opportunities across use cases, sites, and regions • Act as architectural continuity across the customer lifecycle

• Build AI agents, workflows, tools and automations that solve real customer, product and operations problems. • Apply LLMs, retrieval, tool calling, evaluations, guardrails and automation to practical financial workflows. • Create AI-assisted experiences for onboarding, support, KYC, risk review, CRM, operations, document handling and product intelligence. • Work with product and engineering teams to identify what is worth building, then build it quickly and improve it in production. • Design around real constraints: accuracy, hallucination risk, latency, cost, security, compliance and user trust. • Turn manual processes into reliable AI-enabled systems that can scale.