2 months ago
London, United KingdomSenior
Responsibilities
- Serve as a trusted technical advisor and lead architecture reviews, technical presentations, and proof-of-concept efforts.
- Partner with sales and business development to qualify opportunities, respond to RFPs/RFIs, scope and estimate engagements, and shape proposals and statements of work.
- Design and facilitate client workshops covering discovery, use-case ideation, AI readiness, solution design, and enablement.
- Design end-to-end AI solutions involving generative AI, NLP, computer vision, multimodal applications, data pipelines, deployment frameworks, and system integrations.
- Architect secure AI and data platforms for real-time inference, batch processing, and scalable enterprise workloads across hybrid-cloud and multi-cloud environments.
- Incorporate data privacy, responsible AI, governance, observability, and security controls into solution architectures.
- Define AI development and operations standards, contribute to strategic roadmaps, mentor technical teams, and share AI knowledge internally and externally.
- Evaluate emerging AI tools, frameworks, and methodologies and recommend production-ready solutions.
- Collaborate with data scientists, software engineers, product managers, and business leaders to define requirements and deliver client value.
Requirements
- Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field.
- Master's or PhD in Artificial Intelligence, Machine Learning, Computer Science, or a related discipline.
- 7–10 years of experience in solution architecture, software engineering, or related technical roles, including 4–5 years focused on AI/ML.
- Proven experience designing and deploying production AI solutions involving generative AI, NLP, and computer vision.
- Strong understanding of the AI solution lifecycle from business case and requirements gathering through deployment and monitoring.
- Demonstrable client-facing consulting experience, including pre-sales support, scoping, estimation, proposals, demos, or proof-of-concepts.
- Experience designing and facilitating workshops for mixed technical and non-technical audiences.
- Strong communication, stakeholder influence, and commercial awareness of engagement economics.
- Experience in global delivery teams or consulting/professional-services environments.
- Professional certifications such as AWS Certified Machine Learning Specialty, Google Cloud ML Engineer, or Azure AI Engineer Associate.
Tech Stack
Categories
Solutions Engineering
