At a Glance
- Tasks: Own the data infrastructure for AI deployments and ensure seamless integration with business systems.
- Company: Global FinTech leader with a dynamic hybrid work culture.
- Benefits: Competitive salary, excellent benefits, and flexible working arrangements.
- Other info: Exciting opportunities for career growth in a fast-paced environment.
- Why this job: Join a cutting-edge team and shape the future of AI in finance.
- Qualifications: 3-5 years in software or data engineering with strong Python and SQL skills.
The predicted salary is between 80000 - 130000 £ per year.
Our client is a Global FinTech with offices around the world including Bristol and London in the UK. This AI Deployment Engineer role can be based out of Bristol or London offices. Ideally, 3 days per week in the office but could be a little bit more flexible for the ideal candidate.
Salary: £80,000 - £130,000 p/a dependent on experience + excellent benefits.
You will be the deep technical engine behind their internal AI deployment team. Where the AI Deployment Strategist scopes and builds agents, you own the infrastructure underneath — data pipelines, system integrations, and everything that ensures deployed AI solutions work reliably at scale. You will take prototypes to production, ensure all business systems communicate correctly, and build the technical backbone that our AI tooling depends on.
Job Responsibilities
- Own the data infrastructure underpinning AI deployments — pipelines, storage, and data serving.
- Integrate AI solutions into the existing business ecosystem: CRMs, ERPs, SaaS tools, and internal systems.
- Build and maintain APIs, webhooks, and middleware that allow AI agents to interact with business systems.
- Take Strategist-built prototypes to production-grade — hardening, scaling, and ensuring reliability.
- Set up monitoring, logging, and alerting across deployed pipelines and agent infrastructure.
- Manage data models, schemas, and storage supporting current and future AI deployments.
- Troubleshoot integration failures, data inconsistencies, and production issues.
Key Skills
- Python & SQL (production-grade pipeline development).
- REST APIs, webhooks, OAuth, event-driven architecture.
- Orchestration tools: Airflow, Prefect, or Dagster.
- Cloud platforms: AWS, GCP, or Azure.
- Docker & Kubernetes.
- Microsoft 365 & Microsoft Copilot.
Desirable Skills
- Vector databases and embedding pipelines.
- Real-time streaming (Kafka, Flink).
- RPA tooling (UiPath, Power Automate).
- dbt for data transformation.
- Claude Code, Claude Cowork, or Claude Skills.
Experience
- 3–5 years in software or data engineering with strong exposure to system integrations, data pipelines, and production infrastructure.
- Strong Python and SQL skills; experienced building robust, production-grade data pipelines from scratch.
- Deep familiarity with integration patterns: REST APIs, webhooks, OAuth, and event-driven architectures.
- Experience with orchestration tools (Airflow, Prefect, or Dagster) and transformation frameworks (dbt or similar).
- Comfortable across cloud platforms (AWS, GCP, or Azure) and with containerisation (Docker, Kubernetes).
- Experience connecting disparate business systems — SaaS platforms, internal databases, and third-party APIs — and making them work reliably.
- Strong debugging instincts and a high bar for reliability and data integrity.
- Comfortable with Microsoft 365 and Microsoft Copilot. Familiarity with AI productivity tools including Claude Code, Claude Cowork, and Claude Skills is a plus.
Qualifications
- Bachelor's degree in Computer Science, Engineering, or equivalent practical experience.
AI Deployment Engineer employer: Emponics
As a leading Global FinTech, our client offers an exceptional work environment in vibrant cities like Bristol and London, where innovation meets flexibility. Employees enjoy a hybrid working model, competitive salaries, and a culture that fosters professional growth through continuous learning and collaboration on cutting-edge AI technologies. With a commitment to employee well-being and a focus on impactful projects, this is an ideal place for those seeking meaningful and rewarding careers in the fast-evolving financial technology landscape.
StudySmarter Expert Advice🤫
We think this is how you could land AI Deployment Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at local meetups. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio or GitHub repository showcasing your projects, especially those related to AI deployment. This gives potential employers a taste of what you can do beyond your CV.
✨Tip Number 3
Prepare for interviews by practising common technical questions and scenarios related to AI deployment. We recommend doing mock interviews with friends or using online platforms to get comfortable.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace AI Deployment Engineer
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the AI Deployment Engineer role. Highlight your experience with Python, SQL, and any orchestration tools you've used. We want to see how your skills match up with what we're looking for!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about AI deployment and how your background makes you the perfect fit. Keep it engaging and relevant to the job description.
Showcase Your Projects:If you've worked on any relevant projects, make sure to mention them! Whether it's building data pipelines or integrating systems, we love to see real-world examples of your work that demonstrate your skills.
Apply Through Our Website:We encourage you to apply through our website for the best chance of getting noticed. It’s super easy, and you'll be able to keep track of your application status. Plus, we love seeing applications come directly from our site!
How to prepare for a job interview at Emponics
✨Know Your Tech Inside Out
Make sure you’re well-versed in the key technologies mentioned in the job description, like Python, SQL, and cloud platforms. Brush up on your knowledge of REST APIs and orchestration tools like Airflow or Prefect, as these will likely come up during technical discussions.
✨Showcase Your Problem-Solving Skills
Be prepared to discuss specific challenges you've faced in previous roles, especially around data pipelines and system integrations. Use the STAR method (Situation, Task, Action, Result) to structure your answers and highlight how you tackled those issues effectively.
✨Understand the Business Context
Since this role is within a FinTech company, it’s crucial to understand how AI can impact financial services. Familiarise yourself with common industry challenges and think about how your skills can help solve them. This will show your potential employer that you’re not just technically savvy but also business-minded.
✨Ask Insightful Questions
Prepare a few thoughtful questions to ask at the end of your interview. Inquire about the team dynamics, the current projects they’re working on, or how they measure success for the AI Deployment Engineer role. This demonstrates your genuine interest in the position and helps you assess if it’s the right fit for you.
