Senior Applied AI Engineer

How You’ll Make an Impact:

• Design, build, and deploy production-grade AI and LLM-powered features within our intelligent platform
• Improve model performance, reliability, observability, and cost efficiency in enterprise-scale production environments
• Collaborate with product and engineering teams to translate business requirements into scalable AI solutions
• Advance our provider-agnostic AI architecture to enable secure access to multiple LLM ecosystems
• Build internal AI tooling that enhances engineering productivity and broader employee workflows
• Establish best practices for model evaluation, prompt engineering, testing, and governance
• Ensure responsible AI implementation aligned with data privacy, security, and compliance standards
• Design AI systems with strong guardrails around privacy, auditability, global regulatory requirements, and long-term maintainability
• Contribute to architectural decisions as we evolve our modern data stack and AI platform capabilities
• Mentor engineers and raise the technical bar for applied AI across the team
• Evaluate emerging AI capabilities rigorously, adopting what delivers measurable value while avoiding unnecessary complexity or hype

What We’re Looking For:

• A proactive and strategic thinker with strong analytical and problem-solving skills.
• 5+ years of experience building and deploying AI/ML systems in production environments.
• Strong hands-on experience with LLM integration, prompt engineering, model evaluation, and performance optimisation.
• Experience building AI systems where reliability, compliance, and uptime are business-critical.
• Proficiency in Python and experience working with modern AI frameworks and libraries.
• Experience building systems in cloud environments such as AWS, Azure, or GCP. AWS preferred.
• Strong understanding of data architecture, APIs, and scalable backend systems.
• Experience working in product-focused engineering teams, not just research environments.
• Ability to balance innovation with product stability, cost discipline, and measurable business impact.
• Comfort explaining AI trade-offs and feasibility clearly to both technical and non-technical stakeholders.
• Experience working across timezones and collaborating with global teams.
• Bachelor’s in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
• Adaptability to thrive in a fast-changing environment while navigating challenges collaboratively.

Bonus Points For Experience With:

• Vector databases, retrieval-augmented generation (RAG), or model orchestration frameworks.
• Databricks, Snowflake, or modern lakehouse architectures.
• Healthcare, research, or data-intensive domains.