AI Solutions Architect

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Our AI Solutions Architect - Build Our In-House AI Environment

About Us

We’re an ambitious technology company using advanced machine learning (ML) and natural language processing (NLP) to power next-generation products. Until now, our AI projects have relied on third-party engines deployed via Docker - but it’s time to take the next step. We’re ready to build our own in-house AI environment, giving us full control over models, data, and innovation pipeline.

The Role

As our first senior AI hire, you’ll take ownership of designing and building the foundation of our AI ecosystem. You’ll shape the infrastructure strategy, establish our MLOps pipelines, and work closely with product and data teams to enable seamless model development and deployment. Once the environment is established, you’ll play a key role in recruiting and mentoring two mid-level AI engineers who will join your team.

Responsibilities

• Architect, build, and maintain an in-house AI environment (on-prem or hybrid cloud).
• Design MLOps workflows for training, deploying, and monitoring models.
• Integrate and manage containerized AI engines (Docker/Kubernetes).
• Establish best practices for model versioning, data pipelines, and reproducibility.
• Collaborate with ML and NLP researchers to optimise infrastructure for experimentation.
• Set up CI/CD pipelines, monitoring tools, and scalable compute infrastructure.
• Lead future recruitment and mentoring of additional AI engineers.

Skills & experience

Required

• 5+ years of experience in machine learning, data science, or AI system design.
• Proven track record of deploying ML models or LLM-based applications to production.
• Strong programming fundamentals, including data structures and algorithms.
• Hands-on experience with transformers, embeddings, and vector databases.
• Experience running AI workloads on offline or on-premise platforms (non-cloud environments).
• Solid understanding of data pipelines, APIs, and scalable system architecture.

Preferred

• Experience leading small teams or mentoring other engineers.
• Familiarity with MLOps tools and best practices.
• Background in integrating AI solutions into enterprise products.
• Awareness of privacy, bias mitigation, and model explainability techniques.

What We Offer

• Opportunity to design and own the company’s AI infrastructure from the ground up.
• Work with cutting-edge AI technologies in a hands-on, high-impact role.
• Leadership path - build your own AI engineering team.
• Competitive salary (up to £120k), flexible working, and professional growth opportunities.

Ready to build the foundation of our AI future? Apply now and help us shape an intelligent, scalable, and independent AI ecosystem.

• 5+ years of experience in machine learning, data science, or AI system design.
• Proven track record of deploying ML models or LLM-based applications to production.
• Strong programming fundamentals, including data structures and algorithms.
• Hands-on experience with transformers, embeddings, and vector databases.
• Experience running AI workloads on offline or on-premise platforms (non-cloud environments).
• Solid understanding of data pipelines, APIs, and scalable system architecture.

Preferred

• Experience leading small teams or mentoring other engineers.
• Familiarity with MLOps tools and best practices.
• Background in integrating AI solutions into enterprise products.
• Awareness of privacy, bias mitigation, and model explainability techniques.

• Opportunity to design and own the company’s AI infrastructure from the ground up.
• Work with cutting-edge AI technologies in a hands-on, high-impact role.
• Leadership path - build your own AI engineering team.
• Competitive salary (up to £120k), flexible working, and professional growth opportunities.

We’re an ambitious technology company using advanced machine learning (ML) and natural language processing (NLP) to power next-generation products. Until now, our AI projects have relied on third-party engines deployed via Docker - but it’s time to take the next step. We’re ready to build our own in-house AI environment, giving us full control over models, data, and innovation pipeline.