Forward Deployed AI Engineer (all genders)

## What you do. Key Responsibilities AI Implementation & Deployment (ca. 75%) - You design, build, and deploy production-ready AI solutions that deliver measurable business impact — from intelligent automation and AI agents to predictive analytics and generative AI applications - You move fast, taking ideas from prototype to production, using modern frameworks (e.g. LangChain, AutoGen) and foundation models (OpenAI, Anthropic, open-source LLMs) - You integrate AI into existing business systems via APIs and data pipelines, ensure safe and reliable operation through governance and monitoring, and optimise performance and costs for commercial viability - You work embedded within client organisations to understand real processes, drive adoption, and enable AI-led change Business Process Transformation (15%) - Map current business processes understanding how work gets done today, identifying bottlenecks, and spotting automation opportunities - Design AI-enabled future states working with your Deployment Strategist and client stakeholders to reimagine processes with AI capabilities - Assess AI feasibility evaluating which problems are well-suited for AI, which approaches to use, and what ROI to expect - Define success metrics working with your strategist partner to establish measurable outcomes that demonstrate business value - Collaborate on use case prioritisation helping identify which AI opportunities deliver the highest impact relative to implementation complexity Technical Pre-Sales & Demonstration (10%) - Build rapid AI prototypes creating working demonstrations of AI capabilities in prospect-specific contexts within days - Demonstrate AI possibilities showing prospective clients what AI can do for their specific business challenges with concrete examples - Provide technical credibility establishing trust with client technical and business leaders through demonstrated AI expertise - Assess client AI readiness evaluating data quality, technical infrastructure, and organisational capability to support AI initiatives ## What you bring along. - Strong AI/ML implementation experience (2+ years) building and deploying production AI systems, with hands-on expertise in LLMs and generative AI (GPT-4, Claude, Llama), including prompt engineering, RAG, fine-tuning, and AI agent development (LangChain, AutoGen, CrewAI or similar) - Strong Python skills, solid data engineering capabilities (pipelines, vector databases, ETL), API and integration experience, and practical ML foundations (scikit-learn, PyTorch, TensorFlow) - Proven experience shipping AI systems to production, with cloud infrastructure knowledge (AWS, Azure, GCP incl. GPU/serverless), full-stack development around AI applications, DevOps/MLOps practices (CI/CD, monitoring, model versioning), and performance optimisation across cost, latency, and scalability - Strong business outcome focus, rapid iteration mindset, pragmatic problem-solving, and clear communication with non-technical stakeholders - Comfortable with change management, close collaboration with a Deployment Strategist, and selecting the right AI approach for the business problem - Speed-oriented and autonomous execution, learning agility in a fast-evolving AI landscape, travel flexibility, entrepreneurial mindset, and ethical AI awareness (safety, bias, privacy) Essential Requirements - University degree in Computer Science, AI/ML, Data Science, or related technical field (or equivalent practical experience) - Portfolio of production AI/ML systems you've built and deployed - Fluent English (written and spoken) - additional languages (German, Spanish) are valuable for DACH and Spain regions - Legal right to work in the region of employment (UK and EU)