At a Glance
- Tasks: Design and implement cutting-edge AI architectures that drive business success.
- Company: Join a forward-thinking tech company leading the AI revolution.
- Benefits: Attractive salary, flexible working options, and opportunities for professional growth.
- Other info: Dynamic team environment with endless learning and career advancement opportunities.
- Why this job: Be at the forefront of AI innovation and make a real difference in technology.
- Qualifications: Experience in AI architecture, cloud services, and strong collaboration skills.
The predicted salary is between 80000 - 100000 € per year.
Your Responsibilities:
- Define the enterprise AI architecture vision and reference patterns; align them to business goals, risk posture, and engineering standards across cloud and hybrid environments.
- Design secure, scalable AI solutions covering data ingestion, feature engineering, model training, inference, and continuous feedback loops.
- Establish integration patterns (APIs, events, microservices) to embed model-powered capabilities into existing platforms with clear service boundaries.
- Define enterprise-wide AI architecture guidelines, reusable components, and long-term roadmap to ensure consistency and acceleration of AI initiatives.
- Implement MLOps/LLMOps pipelines for versioning, CI/CD, approvals, and controlled promotion across environments; enforce reproducibility.
- Work closely with product owners, data scientists, engineers, security teams, and business stakeholders to ensure architecture translates into high-value solutions.
- Enforce IAM least-privilege with IAM Conditions, organisation policies, and scoped service accounts; integrate BeyondCorp for zero-trust access.
- Operationalise observability using Cloud Logging, Cloud Monitoring, Error Reporting, Trace, and Profiler; build model/LLM telemetry dashboards and alerts.
- Identify the right AI/ML frameworks, cloud services, model orchestration tools, and infrastructure components that align with business needs and scalability goals.
Essential skills/knowledge/experience:
- Design agentic AI architectures using multi-agent orchestration patterns (planner-executor, supervisor-worker, tool-using agents).
- Define reference architectures for enterprise agent platforms integrating LLMs with systems of record (core banking, CRM, risk, payments).
- Design audit-ready agent interactions, tool usage logs, and decision provenance.
- Select and standardize frameworks (e.g., LangGraph, Google ADK, MCP, A2A patterns).
- Hands-on expertise with agentic frameworks (orchestrators).
- Experience with LLMs, prompt engineering, tool/function calling, memory management.
- API-first integration, event-driven architectures, and data pipelines.
- Exposure to AI quality metrics: task success rate, groundedness, containment, FCR.
- Experience on Google Cloud Platform (preferred) or equivalent hyperscale.
- Deep understanding of LLMs, generative AI, RAG patterns, vector databases, embeddings, and prompt/guardrail engineering.
Artificial Intelligence Architect employer: Vallum Associates
As an Artificial Intelligence Architect at our company, you will thrive in a dynamic and innovative work culture that prioritises collaboration and continuous learning. We offer competitive benefits, including professional development opportunities and a commitment to employee well-being, all set in a vibrant location that fosters creativity and growth. Join us to be part of a forward-thinking team dedicated to shaping the future of AI solutions while enjoying a supportive environment that values your contributions.
StudySmarter Expert Advice🤫
We think this is how you could land Artificial Intelligence Architect
✨Tip Number 1
Network like a pro! Reach out to folks in the AI space on LinkedIn or at industry events. We all know that sometimes it’s not just what you know, but who you know that can help you land that dream job.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your AI projects, especially those involving LLMs and multi-agent orchestration. We want to see your hands-on expertise in action, so make it easy for potential employers to see what you can do.
✨Tip Number 3
Prepare for interviews by brushing up on common AI architecture questions. We recommend practising how to explain complex concepts simply, as you’ll likely need to communicate with product owners and business stakeholders who may not be as technical.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got loads of opportunities waiting for talented individuals like you. Plus, it’s a great way to ensure your application gets the attention it deserves.
We think you need these skills to ace Artificial Intelligence Architect
Some tips for your application 🫡
Tailor Your Application:Make sure to customise your CV and cover letter to highlight how your skills align with the responsibilities listed in the job description. We want to see how you can bring your unique experience to our team!
Showcase Relevant Experience:When detailing your past roles, focus on experiences that relate directly to AI architecture and the specific technologies mentioned. We love seeing concrete examples of your work with LLMs, APIs, and cloud services.
Be Clear and Concise:Keep your application straightforward and to the point. Use bullet points where possible to make it easy for us to scan through your qualifications and achievements quickly.
Apply Through Our Website:Don’t forget to submit your application through our website! It’s the best way for us to receive your details and ensures you’re considered for the role. We can’t wait to hear from you!
How to prepare for a job interview at Vallum Associates
✨Know Your AI Architecture Inside Out
Make sure you’re well-versed in the enterprise AI architecture vision and reference patterns. Be ready to discuss how your experience aligns with business goals and engineering standards, especially in cloud and hybrid environments.
✨Showcase Your Design Skills
Prepare to talk about your experience designing secure and scalable AI solutions. Bring examples of data ingestion, feature engineering, and model training that you've worked on, and be ready to explain how you implemented continuous feedback loops.
✨Integration is Key
Familiarise yourself with integration patterns like APIs and microservices. Be prepared to discuss how you’ve embedded model-powered capabilities into existing platforms, ensuring clear service boundaries and effective communication with product owners and engineers.
✨MLOps Mastery
Demonstrate your understanding of MLOps/LLMOps pipelines. Talk about your experience with versioning, CI/CD processes, and how you ensure reproducibility across environments. Highlight any specific tools or frameworks you’ve used to operationalise observability.
