GenAI Solution Architect (Europe)

Gramian Consultancy is a boutique consultancy specializing in IT professional services and engineering talent solutions. With a strong background in software engineering and leadership, we help companies build high-performing teams by matching them with professionals who truly fit their needs. Role Overview We are looking for Solution Engineers to partner directly with customers and lead the end-to-end delivery of high-impact technical solutions. Successful candidates will need to be able to work with customer teams, translating real-world challenges into production-ready systems that leverage Generative AI, Computer Vision, and Machine Learning. This role is a blend of software engineering, ML engineering, architecture, and consulting. Engineers will design and deploy solutions, integrate models, build custom workflows, and guide customers through successful implementation. Commitments Required: 8 hours per day with an overlap of 4 hours with PST. Employment type: Contractor assignment (no medical/paid leave); 100% REMOTE Duration of contract: 6+ months Locations: Europe, preference for German speakers Interview: Technical Assessment, Technical Interview, Cultural Interview Responsibilities You will lead the backend and AI components of a new, HIPAA-compliant clinical intelligence platform in the urology domain. The product ingests data from Electronic Health Records, performs advanced semantic search over structured and unstructured clinical content, and supports workflow automation for medical teams. This is a greenfield build, offering the rare chance to architect foundational systems from scratch alongside the founder. Engage directly with enterprise and strategic customers to understand their workflows, data, and technical requirements. Architect, build, and deploy custom solutions leveraging GenAI, LLMs, Machine Learning and Vision models, and customer data sources. Lead full project lifecycles: scoping, solution design, development, implementation, testing, deployment, and iteration. Integrate and optimize AI/ML pipelines, including data preprocessing, prompt engineering, model selection, and evaluation. Build reliable, scalable software integrations using APIs, cloud services, and containerized systems. Troubleshoot complex technical issues across the stack—applications, models, data pipelines, infrastructure, and integrations. Act as the customer’s trusted technical advisor, enabling adoption of new product capabilities and AI features. Partner closely with internal product and engineering teams to communicate customer feedback and shape roadmap direction. Produce high-quality documentation, architecture diagrams, runbooks, and technical assets for customer teams. Mentor junior engineers and contribute to internal best practices for FDE delivery. 5–10+ years in engineering roles such as Forward Deployed Engineer, ML Engineer, Software Engineer, Solutions Engineer, Technical Consultant, or similar. German language proficiency preferred Strong proficiency in Python, JavaScript/TypeScript, Go, or similar production-oriented languages. Hands-on experience with Machine Learning, including training, fine-tuning, evaluating, or deploying models. Direct experience with Generative AI (LLMs, multimodal models, vector databased, or RAG) and applying them to real-world problems. Exposure to Computer Vision techniques (detection, segmentation, OCR, embeddings, multimodal pipelines). Strong knowledge of ML frameworks (PyTorch, TensorFlow, OpenCV, etc.). Experience with cloud infrastructure (AWS, GCP, Azure) and containerization (Docker, Kubernetes). Excellent communication skills with both technical and non-technical audiences. Comfort leading customer-facing engagements and guiding stakeholders through ambiguity. Willingness and ability to travel frequently. Prior experience in consulting, technical solutions, professional services, or customer-embedded technical roles. Experience with vector databases, embedding pipelines, or retrieval-augmented generation (RAG). Experience building APIs, microservices, or distributed systems. Familiarity with MLOps tools (Docker, Kubernetes, model registries, CI/CD for ML). Background in deploying or fine-tuning CV models (YOLO, SAM, CLIP, DETR, etc.). Experience in startup or high-growth environments.