<div class="show-more-less-html__markup show-more-less-html__markup--clamp-after-5 relative overflow-hidden"> <p><strong>Job Description</strong></p><p>QuantumBasel's business and research applications accelerate the way Quantum and AI technologies can transform industries. We focus on the intersection between Quantum and AI technologies and leverage simple and fast access to the world's best Quantum computers - both on-premise and via the cloud – plus GPUs and CPUs. QuantumBasel’s industry-agnostic focus is helping to transform industries. Today’s customers range from logistics, healthcare and life sciences, energy, manufacturing, and financial services.</p><p><br/></p><p>We are looking for a hands-on technical leader who bridges customer needs and AI-driven solutions - designing, building, and deploying intelligent systems across the full stack, from raw data to production infrastructure for customers. The balance between AI and quantum shifts with each engagement. Some projects are cloud and machine learning end to end; others centre on quantum algorithms running on real hardware. Cloud platforms vary by client, so the role is deliberately platform-agnostic.</p><p><br/></p><p><strong>Key Responsibilities</strong></p><ul><li>Design and build business-scale cloud architectures for clients. Target-state design, Infrastructure as Code, and systems that scale and are agile/future proof.</li><li>Build AI systems on cloud across the full lifecycle: from data and training pipelines, retrieval-augmented generation, agentic workflows to model serving and MLOps.</li><li>Support quantum algorithms by optimizing the problem to run circuits on simulators and QPUs.</li><li>Translate business problems into quantum and hybrid quantum-classical approaches in areas such as optimization, logistics, and financial risk, with a clear view of where current hardware delivers value.</li><li>Work alongside client engineering and stakeholders: review code, shape technical decisions, and bring their teams up to speed.</li><li>Select technologies on merit. Where quantum is not the right fit, say so and set out the alternative.</li></ul><p><br/></p><p><strong>What We’re Looking For</strong></p><p>Design and deliver end-to-end AI solutions spanning classical machine learning and modern GenAI (LLMs, RAG pipelines, agents). Translate customer requirements into scalable, production-ready architectures. Advise on data modeling, pipeline design, and data quality processes; implement AI services, APIs, orchestration, monitoring, and workflow automation; and support scalable deployments through CI/CD, containerization, cloud infrastructure, and infrastructure-as-code.</p><p><br/></p><p><strong>Technologies and methods</strong></p><ul><li>No single engagement uses all of these, but the role draws on a broad stack:</li><li>Languages: Python (primary), SQL, Bash; TypeScript useful.</li><li>AI and ML: PyTorch or TensorFlow, Hugging Face, scikit-learn, vector stores (pgvector, Pinecone), model serving (vLLM, Triton, KServe), MLflow.</li><li>Agentic AI: tool-using agents and orchestration (e.g. LangGraph, LangChain, LlamaIndex), retrieval-augmented generation, evaluation and guardrails.</li><li>Cloud: AWS, Azure, and GCP, including managed ML services such as Vertex AI, Azure ML, and Bedrock.</li><li>Infrastructure: Kubernetes, Docker, Terraform, CI/CD pipelines, and observability with Prometheus, Grafana, and OpenTelemetry.</li><li>Quantum: Qiskit and PennyLane; hardware access via API and our own IonQ system; basic knowlegde on algorithms including QAOA, VQE, and QUBO formulations.</li><li>Architecture: event-driven and distributed systems, microservices, domain-driven design, API design (REST, gRPC, async messaging), and reliability patterns such as SLOs, autoscaling, and graceful degradation.</li><li>Delivery: Agile teams, trunk-based development, infrastructure as code, automated testing, and security by design.</li></ul><p><br/></p><p><strong>Requirements</strong></p><ul><li>3–5 years delivering production software, with strong depth in at least one major cloud platform.</li><li>Advanced Python, including asynchronous programming and a disciplined approach to testing.</li><li>Demonstrated experience taking machine learning or LLM systems into production, including retrieval-augmented generation and agentic patterns.</li><li>Working knowledge of quantum computing: circuit-level programming, the limits of current NISQ hardware, and the problem classes it suits.</li><li>A solid grounding in distributed and event-driven systems, with a record of designing for reliability and scale.</li><li>Ownership of infrastructure as code and CI/CD as part of day-to-day delivery.</li><li>The ability to work independently on client sites and explain technical trade-offs to both engineers and decision-makers.</li><li>Holding a Swiss/EU passport, a valid EU/EFTA work permit, or a valid Swiss residence permit (B/ C permit).</li></ul><p><br/></p><p><strong>Preferred qualifications</strong></p><ul><li>Certifications across cloud (AWS, Azure, or GCP) or quantum platforms.</li><li>Consulting or other client-facing delivery experience.</li><li>Open-source contributions to ML, agentic, or quantum tooling.</li><li>Familiarity with domain-driven design and streaming platforms such as Kafka or Pub/Sub.</li></ul> </div>