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- Tasks: Build next-gen AI systems and design workflows for a digital assistant processing millions of queries.
- Company: Join a leading financial services organisation at the forefront of AI innovation.
- Benefits: Competitive pay up to ÂŁ700 per day, flexible contract work, and opportunities for growth.
- Why this job: Make a real impact in AI while working with cutting-edge technologies and talented teams.
- Qualifications: Strong Python skills, experience with microservices, and a focus on production systems.
- Other info: Collaborative environment with a focus on scalability and reliability in AI services.
AI Connect are partnering with a leading financial services organisation to help them hire an AI Engineer to work on next-generation agentic AI systems. This role sits at the intersection of software engineering and applied AI building production‑grade, observable, and scalable agent‑based systems. You’ll focus on designing and delivering agentic workflows, robust evaluation frameworks, and real‑world AI services for a digital assistant that processes millions of queries daily.
What You’ll Do
- Build and evolve agentic AI systems using frameworks such as LangGraph, focusing on orchestration, state management, and multi‑step reasoning.
- Design and implement evaluation frameworks for LLM‑powered systems.
- Implement observability and monitoring for agentic workflows using tools like Langfuse, ensuring traceability, reliability, and performance insight.
- Develop production‑grade microservices using Python, FastAPI, and modern software engineering practices.
- Integrate LLM‑powered services with internal APIs, tools, and data sources to enable real‑world decision‑making and automation.
- Work closely with product, platform, and engineering teams to take agentic solutions from concept through to production.
- Contribute to architectural decisions around scalability, reliability, and system design for AI‑powered services.
Key Skills & Experience
- Strong experience as a Software Engineer or AI Engineer, with a clear focus on production systems rather than research.
- Excellent Python engineering skills, including experience building APIs and services with FastAPI (or similar frameworks).
- Solid understanding of microservices architecture, RESTful APIs, and distributed systems.
- Hands‑on experience with LangGraph for building agentic workflows (this is a key requirement).
- Experience designing or working with LLM evaluation frameworks, testing strategies, and quality measurement.
- Experience implementing observability for LLM systems, ideally using Langfuse or similar tooling.
- Comfortable working in cloud environments (AWS experience is highly desirable).
- Strong communication skills and the ability to collaborate with cross‑functional teams.
Artificial Intelligence Engineer employer: AI Connect | Data & AI Delivery Partner
Contact Detail:
AI Connect | Data & AI Delivery Partner Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Artificial Intelligence Engineer
✨Tip Number 1
Network like a pro! Reach out to your connections in the AI and software engineering fields. Attend meetups, webinars, or even online forums where you can chat with industry folks. You never know who might have the inside scoop on job openings!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving Python, FastAPI, and LangGraph. Having tangible examples of your work can really set you apart when you're chatting with potential employers.
✨Tip Number 3
Prepare for interviews by brushing up on common AI and software engineering questions. Practice explaining your thought process when designing agentic workflows or implementing observability. Confidence is key, so get comfortable talking about your experience!
✨Tip Number 4
Don’t forget to apply through our website! We’ve got loads of opportunities that might be perfect for you. Plus, applying directly can sometimes give you a better chance of getting noticed by hiring managers.
We think you need these skills to ace Artificial Intelligence Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the AI Engineer role. Highlight your experience with Python, FastAPI, and any work you've done with LangGraph. We want to see how your skills match what we're looking for!
Showcase Your Projects: Include specific projects where you've built production-grade systems or worked with LLM evaluation frameworks. This gives us a clear picture of your hands-on experience and how you tackle real-world challenges.
Be Clear and Concise: When writing your application, keep it clear and to the point. Use bullet points for key achievements and avoid jargon unless it's relevant. We appreciate straightforward communication!
Apply Through Our Website: Don't forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. We can’t wait to see what you bring to the table!
How to prepare for a job interview at AI Connect | Data & AI Delivery Partner
✨Know Your Tech Inside Out
Make sure you’re well-versed in the technologies mentioned in the job description, especially Python and FastAPI. Brush up on your experience with LangGraph and be ready to discuss how you've used it to build agentic workflows.
✨Showcase Your Problem-Solving Skills
Prepare to share specific examples of how you've tackled challenges in building production-grade systems. Think about times when you had to implement observability or design evaluation frameworks, and be ready to explain your thought process.
✨Communicate Clearly and Collaboratively
Since this role involves working closely with cross-functional teams, practice articulating your ideas clearly. Be prepared to discuss how you’ve collaborated with product and engineering teams in the past to bring solutions from concept to production.
✨Ask Insightful Questions
At the end of the interview, don’t forget to ask questions that show your interest in the role and the company. Inquire about their current projects involving AI systems or how they measure the success of their agentic workflows.
