At a Glance
- Tasks: Design and build scalable AI architectures for financial services.
- Company: Leading consulting and technology firm transforming AI in banking.
- Benefits: Hybrid work, competitive salary, and opportunities for career advancement.
- Other info: Open to various experience levels, from Manager to Associate Director.
- Why this job: Join a dynamic team shaping the future of AI in finance.
- Qualifications: Experience in AI architecture and strong communication skills required.
The predicted salary is between 70000 - 90000 € per year.
We're supporting a major consulting and technology organisation delivering enterprise AI and data transformation programmes across Financial Services. The focus is designing scalable AI architectures that move beyond proof of concept into secure, production-grade deployment across banking environments.
What you'll be doing:
- Designing AI and GenAI architecture strategies and roadmaps
- Building scalable AI/ML and Agentic AI platforms
- Working across LLMs, RAG, embeddings and semantic search
- Supporting AI deployment, governance and integration patterns
- Collaborating with engineering, data, DevOps and business teams
- Advising clients on AI platform capability and implementation strategy
Environment:
- Python
- LLMs, prompt engineering, fine-tuning, RAG
- LangChain, LangGraph, Agent frameworks
- Vector databases and semantic search
- MLOps and LLMOps
- AWS, Azure, GCP, Databricks
- Containerisation, Kubernetes and GPU infrastructure
Requirements:
- Experience designing enterprise AI or ML architectures
- Strong understanding of cloud and modern data platforms
- Exposure to scalable AI deployment and integration patterns
- Financial Services or regulated industry experience essential
- Strong stakeholder engagement and communication skills
- Open to candidates from Manager through to Associate Director level.
If you're open to a confidential conversation about the AI architecture market, please message me directly.
AI Solution Architect - Financial Services Consulting in Dunfermline employer: Datatech Analytics
As a leading consulting and technology organisation, we pride ourselves on fostering a dynamic work culture that encourages innovation and collaboration. Our hybrid working model in Scotland offers employees the flexibility to balance their professional and personal lives while engaging in meaningful projects that drive AI transformation in the financial services sector. With a strong emphasis on employee growth, we provide ample opportunities for career advancement and skill development, making us an excellent employer for those looking to make a significant impact in the industry.
StudySmarter Expert Advice🤫
We think this is how you could land AI Solution Architect - Financial Services Consulting in Dunfermline
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those already working in AI and Financial Services. Attend meetups or webinars, and don’t be shy about sliding into DMs on LinkedIn. You never know who might have the inside scoop on job openings!
✨Tip Number 2
Showcase your skills! Create a portfolio that highlights your experience with AI architectures and projects you've worked on. Use platforms like GitHub to share your code or case studies. This gives potential employers a taste of what you can bring to the table.
✨Tip Number 3
Prepare for interviews by brushing up on common questions related to AI deployment and integration patterns. Be ready to discuss your experience with cloud platforms like AWS or Azure, and how you've tackled challenges in previous roles. Practice makes perfect!
✨Tip Number 4
Don’t forget to apply through our website! We’ve got some fantastic opportunities waiting for you, and applying directly can sometimes give you an edge. Plus, it’s a great way to show your enthusiasm for joining our team!
We think you need these skills to ace AI Solution Architect - Financial Services Consulting in Dunfermline
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that align with the AI Solution Architect role. Highlight your experience in designing scalable AI architectures and any relevant projects you've worked on in the financial services sector.
Craft a Compelling Cover Letter:Use your cover letter to tell us why you're passionate about AI and how your background makes you a great fit for this position. Be specific about your experience with cloud platforms and AI deployment strategies, as these are key to the role.
Showcase Your Technical Skills:Don’t shy away from listing your technical proficiencies! Mention your experience with Python, LLMs, and any tools like Kubernetes or AWS that you've used. This will help us see your fit for the technical aspects of the job.
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 the role. Plus, it shows us you’re keen on joining our team!
How to prepare for a job interview at Datatech Analytics
✨Know Your AI Architecture Inside Out
Make sure you’re well-versed in designing scalable AI architectures. Brush up on your knowledge of LLMs, RAG, and semantic search, as these are key components for the role. Be ready to discuss how you've applied these technologies in past projects.
✨Showcase Your Financial Services Experience
Since experience in financial services is essential, prepare examples that highlight your work in regulated industries. Talk about specific challenges you faced and how you overcame them, demonstrating your understanding of compliance and governance.
✨Engage with Stakeholders
Strong communication skills are a must. Think of instances where you successfully collaborated with engineering, data, and business teams. Be prepared to share how you’ve managed stakeholder expectations and driven projects forward through effective engagement.
✨Familiarise Yourself with Cloud Platforms
Given the focus on cloud technologies like AWS, Azure, and GCP, ensure you can discuss your experience with these platforms. Highlight any projects where you’ve implemented MLOps or LLMOps, and be ready to explain how you approached deployment and integration.
