At a Glance
- Tasks: Lead AI architecture decisions and collaborate on innovative blockchain intelligence projects.
- Company: Join a pioneering tech company focused on financial crime prevention and digital asset risk management.
- Benefits: Enjoy hybrid work, generous leave, health insurance, and a £1,000 learning budget.
- Other info: Flexible remote work options and a supportive culture for professional growth.
- Why this job: Shape the future of AI in a dynamic environment with real-world impact.
- Qualifications: Experience in AI architecture, strong communication skills, and a passion for blockchain technology.
The predicted salary is between 80000 - 100000 £ per year.
Elliptic develops blockchain intelligence technology that helps financial institutions, regulators, and law enforcement agencies identify financial crime and manage digital asset risk. This Staff AI Engineer position joins the company during a foundational phase of AI platform development, where key architectural decisions, agentic workflows, evaluation frameworks, and observability standards are being established. The role operates across multiple engineering initiatives, helping define scalable AI infrastructure, governance frameworks, and production-ready patterns that support customer‑facing AI products. Working closely with engineering and product teams, the successful candidate will influence long-term AI strategy, platform standards, and blockchain infrastructure roles while ensuring reliability, auditability, and operational excellence across AI systems.
Responsibilities
- Act as the architectural authority for early‑stage AI platform decisions and technology evaluations.
- Assess tooling ecosystems, including LangSmith and Databricks, against the requirements of production‑scale AI products and provide evidence‑based recommendations.
- Collaborate with Investigations & AI and AgentForce engineering teams to ensure agentic architectures and evaluation frameworks are designed for customer‑facing scale.
- Maintain an objective and documented AI stack evaluation process, including trade‑offs, decision criteria, and architectural considerations.
- Define and promote engineering standards for AI systems, including observability, tracing, prompt management, version control, cost governance, evaluation frameworks, and agent reliability.
- Produce technical foundation documents that clearly outline architecture decisions, deferred considerations, and system capabilities.
- Advocate for AI adoption, engineering best practices, and enablement across product and engineering functions.
Requirements
- Experience making production AI architecture decisions involving evaluation frameworks, LLM integration strategies, prompt management, versioning, and observability.
- Ability to assess technical trade‑offs and guide long‑term architectural direction using evidence‑based decision‑making.
- Experience working across internal tooling platforms and customer‑facing AI products.
- Strong understanding of reliability, auditability, governance, and cost considerations in production AI systems.
- Experience designing or significantly influencing AI evaluation or observability frameworks in production environments.
- Ability to operate effectively as an individual contributor with influence driven through technical leadership rather than people management.
- Strong communication skills with the ability to align stakeholders around architectural decisions.
- Comfortable working across teams in environments with evolving requirements and ambiguity.
- Understanding of explainability, trustworthiness, reliability, and compliance requirements within AI systems.
Bonus Points
- Experience building production‑grade agentic systems, including orchestration frameworks, tool integration, memory management, and reliability mechanisms.
- Familiarity with AI ecosystems such as LangSmith, MLflow, Databricks ML, or similar platforms.
- Experience leading the transition from isolated AI integrations to centralized AI platform architectures.
- Understanding of organizational and technical challenges associated with AI platform adoption.
- Interest in cryptocurrency, blockchain technology, and digital assets.
Compensation & Benefits
- Hybrid work model.
- Work‑from‑almost‑anywhere flexibility for up to 90 days annually.
- £500 remote working budget for home office setup.
- $1,000 annual Learning & Development budget.
- 25 days annual leave plus bank holidays.
- Additional birthday leave day.
- Enhanced parental leave with 16 weeks fully paid leave for eligible employees.
- Private health insurance through Vitality.
- Access to Spill Mental Health Support.
- Life assurance coverage equal to four times salary.
- £100 cryptocurrency benefit.
- Cycle to Work Scheme.
Staff AI Engineer — AI Architecture employer: ArtOfBlockchain
At Elliptic, we pride ourselves on being an exceptional employer, particularly for those passionate about AI and blockchain technology. Our hybrid work model offers unparalleled flexibility, allowing you to work from almost anywhere, while our commitment to employee growth is evident through generous learning budgets and a supportive work culture that prioritises mental health and well-being. Join us in shaping the future of financial crime prevention and digital asset risk management, where your contributions will directly influence our innovative AI platform during this exciting foundational phase.
StudySmarter Expert Advice🤫
We think this is how you could land Staff AI Engineer — AI Architecture
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those already working at Elliptic or similar companies. A friendly chat can give you insider info and maybe even a referral!
✨Tip Number 2
Show off your skills! Create a portfolio or GitHub repo showcasing your AI projects, especially any that relate to blockchain or production-grade systems. This will help us see your hands-on experience and creativity.
✨Tip Number 3
Prepare for the interview by brushing up on your architectural decision-making skills. Be ready to discuss how you've tackled challenges in AI systems and what frameworks you've used. We love hearing about real-world applications!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining our team at Elliptic.
We think you need these skills to ace Staff AI Engineer — AI Architecture
Some tips for your application 🫡
Tailor Your Application:Make sure to customise your CV and cover letter to highlight your experience with AI architecture and production systems. We want to see how your skills align with our needs, so don’t hold back on showcasing relevant projects!
Showcase Your Technical Skills:When you’re writing your application, be specific about the tools and frameworks you’ve worked with, like LangSmith or Databricks. We love seeing evidence-based recommendations and architectural decisions you've made in past roles.
Communicate Clearly:Strong communication is key! Use your application to demonstrate how you’ve aligned stakeholders around architectural decisions in the past. Clear examples of your influence will make you stand out to us.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for this exciting opportunity in our foundational AI platform development phase!
How to prepare for a job interview at ArtOfBlockchain
✨Know Your AI Architecture Inside Out
Before the interview, make sure you’re well-versed in the key architectural decisions and evaluation frameworks relevant to AI platforms. Brush up on your experience with production AI architecture, especially around observability and governance, as these will be crucial topics during your discussion.
✨Showcase Your Collaboration Skills
This role involves working closely with various teams, so be prepared to discuss how you've successfully collaborated in the past. Share specific examples of how you’ve influenced architectural decisions or worked across teams to achieve a common goal, particularly in environments with evolving requirements.
✨Prepare Evidence-Based Recommendations
Since the position requires assessing tooling ecosystems like LangSmith and Databricks, come ready with evidence-based recommendations. Think about past experiences where you evaluated tools and made decisions based on data—this will demonstrate your analytical skills and decision-making process.
✨Communicate Clearly and Confidently
Strong communication is key for this role. Practice articulating your thoughts on complex topics like AI reliability and compliance in a way that’s easy to understand. Be ready to align stakeholders around your architectural decisions, showcasing your ability to simplify technical jargon.
