AI - Solutions Architect

Company Overview:

10Pearls is a global, purpose-driven AI-Native digital engineering partner helping businesses re-imagine, ‎digitalize, and accelerate. As an end-to-end digital technology partner, 10Pearls helps businesses create future-proof, ‎transformative ‎digital products that leverage emerging technologies. ‎10Pearls' clients ‎include Global 2000 enterprises, high growth mid-size ‎businesses, and some of the most exciting ‎start-ups from industries like healthcare, fintech, ‎energy, education, ‎real estate, retail, and hi-tech. ‎Headquartered in the Washington DC metro area, 10Pearls has product engineering and ‎software development centers in North America, Latin America, Europe, and South Asia. To learn more, visit https://10pearls.com.  

Job Overview:

We are seeking an experienced AI Solutions Architect to design, deliver, and help sell cutting-edge artificial intelligence solutions for our clients across a wide range of industries. This is a client-facing role that spans the full engagement lifecycle — from shaping opportunities and supporting pre-sales, through facilitating discovery and design workshops, to owning the end-to-end AI and data platform architecture that gets built.

The ideal candidate brings deep expertise in generative AI, machine learning, data engineering, and scalable infrastructure, combined with the consulting skills to lead workshops, influence stakeholders at all levels, and translate business needs into production-grade solutions. This is a unique opportunity to join a global team of over 1,300 product and engineering professionals and play a leading role in growing our AI practice from our Paddington, London office.

Responsibilities:

• Client & Stakeholder Engagement: Serve as a trusted technical advisor throughout client engagements. Lead architecture reviews, technical presentations, and proof-of-concept efforts for internal and external stakeholders. Articulate solution value and translate business needs into scalable, AI-driven architectures, ensuring alignment across technical and non-technical audiences.
• Pre-Sales & Business Development: Partner with sales and business development to qualify opportunities and win new work. Contribute to RFP/RFI responses, scope and estimate engagements (effort, cost, and timeline), and help shape proposals and statements of work. Design and deliver compelling demos and proof-of-concepts that demonstrate feasibility and value, applying commercial awareness of deal economics alongside technical fit.
• Workshop Facilitation & Enablement: Design and facilitate client-facing workshops, including discovery, use-case ideation, AI-readiness and maturity assessment, solution design, and enablement sessions. Run interactive sessions that draw requirements out of mixed technical and non-technical audiences and drive toward clearly defined outcomes. Upskill client and internal teams on AI concepts and best practices.
• Solution Architecture & Design: Lead the design of end-to-end AI solutions that align with both business objectives and technical requirements, adapting to each client's existing environment and constraints. Develop architecture blueprints covering model development, data pipelines, deployment frameworks, and system integrations — with a strong focus on GenAI, NLP, computer vision, and multimodal AI applications. Leverage deep knowledge of AI/ML frameworks (e.g., PyTorch, TensorFlow, Hugging Face) and enterprise architecture best practices (e.g., microservices, APIs, cloud-native design).
• AI & Data Platform Architecture / Deployment: Architect robust, secure AI and data platforms that support real-time inference, batch processing, and scalable enterprise workloads, integrating with clients' incumbent technology estates. Implement hybrid-cloud and multi-cloud strategies using modern data infrastructure (Kafka, Spark, data lakes), MLOps practices, and deployment platforms. Ensure compliance with data privacy regulations (including UK GDPR) and responsible AI standards, incorporating governance, observability, and security controls into the architecture.
• Strategic AI Leadership: Act as a technical advisor and thought leader on AI strategy and innovation across the organization and with clients. Define standards and frameworks for AI development and operations, contribute to the long-term roadmap, and mentor technical teams on AI best practices. Demonstrate thought leadership through internal knowledge-sharing and, where applicable, public engagements or open-source contributions.
• Technology Evaluation & Innovation: Stay at the forefront of AI innovation, evaluating new tools, frameworks, and methodologies for business impact. Provide clear, evidence-based recommendations that drive innovation while maintaining alignment with enterprise architecture and operational readiness. Translate rapidly evolving GenAI trends into practical, production-grade solutions.
• Cross-Functional Collaboration: Partner with data scientists, software engineers, product managers, and business leaders to define technical requirements and deliver value-driven solutions, facilitating clear and actionable communication of complex AI concepts

Required Qualifications:

• Bachelor's degree in Computer Science, Engineering, Mathematics, or related field
• 7–10 years of experience in solution architecture, software engineering, or related technical roles, with 4–5 years focused on AI/ML
• Proven success designing and deploying AI solutions in production environments, including GenAI, NLP, and computer vision
• Strong understanding of the AI solution lifecycle — from business case and requirements gathering to deployment and monitoring
• Demonstrable client-facing consulting experience, including pre-sales support (scoping, estimation, proposals/SOWs, demos, or POCs)
• Experience designing and facilitating client workshops for mixed technical and non-technical audiences
• Strong communicator with the ability to influence both executive and technical stakeholders, and commercial awareness of engagement economics
• Master's or PhD in Artificial Intelligence, Machine Learning, Computer Science, or related discipline
• Experience working within global delivery teams or a consulting/professional-services environment
• Professional certifications such as AWS Certified Machine Learning Specialty, Google Cloud ML Engineer, or Azure AI Engineer Associate

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