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For Companies At a Glance
- Tasks: Join a team to deliver AI solutions that enhance document handling and decision workflows.
- Company: Be part of a major financial institution's innovative Treasury and Liquidity function.
- Benefits: Enjoy hybrid work options and competitive daily rates, with opportunities for growth.
- Why this job: Shape the future of AI in finance while collaborating with experts in a dynamic environment.
- Qualifications: Experience with LangChain, multi-agent systems, and Azure deployment is essential.
- Other info: Ideal for those passionate about AI and looking to make an impact in financial services.
The predicted salary is between 54000 - 84000 £ per year.
We’re looking for a highly capable Technical AI Business Analyst with hands-on experience delivering production-grade GenAI solutions, particularly those leveraging multi-agent architectures using frameworks like LangChain, LangGraph, and OpenAI’s Agent Framework.
You will join a forward-thinking team within a major financial institution's Treasury and Liquidity function, helping to shape and deliver AI-powered systems that streamline document handling, decision workflows, and internal knowledge processes.
What You’ll Do
- Work closely with AI engineers and Treasury SMEs to define end-to-end use cases involving multi-agent AI systems.
- Translate business needs into detailed technical workflows using LangGraph, ensuring agents interact via clear logic and shared state.
- Shape document-based GenAI solutions, including RAG pipelines, prompt engineering, and integration with internal APIs.
- Help orchestrate tools using OpenAI function calling, vector databases (e.g. Pinecone, Weaviate), and knowledge graphs.
- Collaborate on the deployment of AI agents as secure microservices using Azure Kubernetes Services (AKS) and monitor system performance.
- Draft structured documentation, user manuals, and internal process maps for end users and engineering handover.
Must-Have Skills
- Strong experience with LangChain, LangGraph, or equivalent multi-agent orchestration frameworks.
- Solid understanding of RAG (Retrieval-Augmented Generation) architecture, including chunking strategies, embeddings, and prompt design.
- Proven ability to define and coordinate agent interaction protocols, fallback logic, and tool use within LLM systems.
- Experience integrating OpenAI (or Anthropic) models with enterprise data via APIs and tools.
- Comfortable deploying systems in Azure (AKS, App Services, ML Endpoints) with CI/CD awareness.
- Ability to work with AI engineers and business stakeholders to bridge the gap between conceptual use cases and deployed AI solutions.
- Background in Treasury, Liquidity, or financial services is ideal but not essential if candidate has strong document-based AI solutioning experience.
Technical AI Business Analyst employer: Alba Partners
Contact Detail:
Alba Partners Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Technical AI Business Analyst
✨Tip Number 1
Familiarise yourself with the specific frameworks mentioned in the job description, such as LangChain and LangGraph. Having hands-on experience or even personal projects using these tools can set you apart during discussions with our team.
✨Tip Number 2
Network with professionals in the AI and financial services sectors. Attend relevant meetups or webinars to connect with individuals who might provide insights or referrals that could help you land this role with us.
✨Tip Number 3
Prepare to discuss real-world applications of multi-agent systems and RAG architecture. Be ready to share examples of how you've implemented these concepts in previous roles or projects, as this will demonstrate your practical knowledge.
✨Tip Number 4
Showcase your ability to bridge technical and business needs by preparing a few case studies where you've successfully collaborated with stakeholders. This will highlight your communication skills and understanding of both AI technology and business processes.
We think you need these skills to ace Technical AI Business Analyst
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with GenAI solutions and multi-agent architectures. Include specific projects where you've used frameworks like LangChain or LangGraph, as well as any relevant financial services experience.
Craft a Compelling Cover Letter: In your cover letter, explain why you're passionate about AI and how your skills align with the role. Mention your hands-on experience with RAG architecture and your ability to translate business needs into technical workflows.
Showcase Technical Skills: Clearly outline your technical skills related to the job description. Highlight your familiarity with Azure Kubernetes Services, OpenAI models, and any experience you have with document-based AI solutions.
Prepare for Potential Questions: Think about how you would explain complex concepts like agent interaction protocols or prompt engineering in simple terms. Be ready to discuss your past experiences and how they relate to the responsibilities of the role.
How to prepare for a job interview at Alba Partners
✨Showcase Your Technical Expertise
Be prepared to discuss your hands-on experience with frameworks like LangChain and LangGraph. Highlight specific projects where you've successfully delivered GenAI solutions, focusing on the technical challenges you faced and how you overcame them.
✨Understand the Business Context
Familiarise yourself with the financial institution's Treasury and Liquidity functions. Demonstrating an understanding of how AI can streamline document handling and decision workflows will show that you can bridge the gap between technical and business needs.
✨Prepare for Scenario-Based Questions
Expect questions that assess your problem-solving skills in real-world scenarios. Be ready to explain how you would define end-to-end use cases for multi-agent systems and how you would ensure clear logic and shared state among agents.
✨Emphasise Collaboration Skills
Since the role involves working closely with AI engineers and Treasury SMEs, highlight your experience in collaborative environments. Share examples of how you've effectively communicated technical concepts to non-technical stakeholders and vice versa.
