AI Solutions Consultant

• Partner with enterprise customers to understand their business challenges and identify opportunities where AI can deliver measurable value.
• Lead discovery sessions and qualify opportunities by understanding business goals, stakeholders, priorities, and success criteria.
• Translate business needs into scalable AI solution designs and clearly communicate both the business value and technical approach.
• Present solution concepts, reference architectures, and relevant customer success stories.
• Design high-level solution architectures and assess technical feasibility, implementation scope, and delivery effort.
• Lead technical discussions with customer architects, engineering teams, and technical stakeholders.
• Prepare technical proposals, architecture diagrams, and implementation plans.
• Support customers throughout the pilot and implementation phases, ensuring successful delivery from proof of concept to production.
• Work closely with customer engineering teams to establish deployment, governance, CI/CD pipelines, monitoring, and operational best practices.
• Act as the primary technical advisor throughout the customer engagement, bridging business objectives with technical execution.

Team & Collaboration

• Regular meetings: To be determined.
• Communication: Chat, emails, and calls
• Working hours: Usually 9:00–17:00 CET, with flexibility to adjust working hours occasionally based on client time zones

• Experience in solution architecture, technical consulting, pre-sales engineering, customer engineering, or a similar client-facing technical role.
• Strong understanding of modern software architecture, cloud platforms, APIs, microservices, and distributed systems.
• Experience designing enterprise software or AI solutions.
• Ability to communicate effectively with both executive stakeholders and technical teams.
• Experience gathering requirements, defining solution scope, and translating business needs into technical designs.
• Excellent presentation, communication, and stakeholder management skills.
• Strong analytical and problem-solving abilities.
• Professional level of spoken and written English.

• Experience with Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), or AI agents.
• Experience with Kubernetes, Docker, CI/CD, Infrastructure as Code, or cloud-native architectures.
• Familiarity with MLOps, model deployment, and production AI systems.
• Cloud certifications (AWS, Azure, or Google Cloud).
• Experience working with enterprise customers in regulated industries.
• Experience delivering Proofs of Concept (PoCs) and supporting customers from discovery through production deployment.