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For Companies At a Glance
- Tasks: Join a team to deliver cutting-edge AI solutions in finance.
- Company: Be part of a major financial institution shaping the future of Treasury and Liquidity.
- Benefits: Enjoy hybrid work options and competitive pay ranging from £250 to £600 per day.
- Why this job: Work on innovative projects that streamline processes and make a real impact.
- Qualifications: Experience with LangChain, multi-agent systems, and AI integration is essential.
- Other info: Opportunity to collaborate with top AI engineers and enhance your technical skills.
The predicted salary is between 60000 - 84000 £ per year.
Location: London Hybrid (1-3 days onsite)
Rate - £250 - £600 Per Day inside IR35
Overview
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 showcasing your skills with these tools can set you apart from other candidates.
✨Tip Number 2
Network with professionals in the AI and financial services sectors. Attend relevant meetups or webinars to connect with people who might provide insights into the role or even refer you internally at StudySmarter.
✨Tip Number 3
Prepare to discuss real-world applications of multi-agent systems during interviews. Think of examples where you've successfully implemented similar solutions or how you would approach specific challenges related to document handling and decision workflows.
✨Tip Number 4
Showcase your understanding of the financial sector, particularly Treasury and Liquidity functions. Even if you lack direct experience, demonstrating knowledge of industry challenges and how AI can address them will make a strong impression.
We think you need these skills to ace Technical AI Business Analyst
Some tips for your application 🫡
Understand the Role: Before applying, make sure you fully understand the responsibilities and requirements of the Technical AI Business Analyst position. Familiarise yourself with terms like GenAI, multi-agent architectures, and frameworks such as LangChain and LangGraph.
Tailor Your CV: Highlight your relevant experience in AI solutions, particularly with multi-agent systems and document handling. Use specific examples that demonstrate your hands-on experience with the required technologies and frameworks mentioned in the job description.
Craft a Compelling Cover Letter: Write a cover letter that connects your skills and experiences to the role. Emphasise your ability to work with AI engineers and business stakeholders, and mention any relevant projects that showcase your understanding of RAG architecture and deployment in Azure.
Showcase Your Technical Skills: In your application, be sure to detail your technical skills related to the job. Discuss your experience with integrating OpenAI models, using APIs, and deploying systems in Azure. This will help demonstrate your capability to bridge the gap between conceptual use cases and deployed AI solutions.
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 services sector, particularly 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 business stakeholders, highlight your experience in collaborative environments. Share examples of how you've effectively communicated complex technical concepts to non-technical team members.
