National AI Awards 2025Discover AI's trailblazers! Join us to celebrate innovation and nominate industry leaders.

Nominate & Attend

AI Engineer | GenAI | Fintech

Reqiva
Greater London
2 months ago
Applications closed

Related Jobs

View all jobs

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Senior Machine Learning Engineer

This range is provided by Reqiva. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

Base pay range

Location:Reading, or London, UK, Hybrid

Are you passionate about cutting-edge AI advancements?

Do you have hands-on experience with large language models (LLMs) and agentic systems?

We are partnered with a leading Fintech business who are looking for experienced AI Engineers to lead transformative projects, automating processes and optimising operations within the financial technology landscape. Whether your experience comes from commercial applications or groundbreaking research, this role is your opportunity to make a real-world impact.

About the Role

This is your chance to work at the forefront of AI innovation. As an AI Engineer, you’ll design and implement solutions that harness the power of LLMs and agentic systems. You’ll develop AI-driven automation across vast datasets and streamline processes in an innovative way.

Responsibilities:

  • LLM Integration: Design, fine-tune, and deploy large language models to automate and optimise complex workflows.
  • Agentic Systems: Build autonomous, multi-agent frameworks for dynamic decision-making and large-scale task orchestration.
  • Vector Databases: Develop real-time retrieval systems using tools like Weaviate, Pinecone, and Milvus.
  • AI Automation: Create scalable systems to enhance operational efficiency across compliance, trading, and internal processes.
  • NLP Applications: Implement solutions for sentiment analysis, document processing, and conversational AI using state-of-the-art NLP tools.
  • MLOps and Deployment: Scale AI models seamlessly using frameworks like MLflow, Kubeflow, and Docker.

Key Skills & Expertise:

  • Practical experience with LLMs, including model fine-tuning and deployment.
  • Knowledge of agentic systems and autonomous multi-agent frameworks.
  • Proficiency in Python and deep learning libraries like TensorFlow or PyTorch.
  • Hands-on experience with vector databases (e.g., Weaviate, Pinecone).
  • Familiarity with NLP tools such as Hugging Face Transformers and OpenAI APIs.

Passion for AI:

We’re looking for someone who lives and breathes AI—whether through academic research, innovative side projects, or commercial applications.

This offers:

  • Lead impactful AI projects in a forward-thinking industry.
  • Shape automation and optimisation strategies in a high-tech environment.
  • Join a team dedicated to pushing the boundaries of AI innovation.
  • Competitive salary, growth opportunities, and access to cutting-edge tools.

Please apply for consideration. Applicants must have the right to work in the UK. Unfortunately, visa sponsorship is not available for this role.

Seniority level

Mid-Senior level

Employment type

Full-time

Job function

Information Technology and Engineering

#J-18808-Ljbffr

National AI Awards 2025

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

LinkedIn Profile Checklist for Machine Learning Jobs: 10 Tweaks to Drive Recruiter Interest

The machine learning landscape is rapidly evolving, with demand soaring for experts in modelling, algorithm tuning and data-driven insights. Recruiters hunt for candidates proficient in Python, TensorFlow, PyTorch and MLOps processes. A generic profile simply won’t cut it. Our step-by-step LinkedIn for machine learning jobs checklist covers 10 targeted tweaks to ensure your profile ranks in searches and communicates your technical impact. Whether launching your ML career or seeking leadership roles, these optimisations will sharpen your professional narrative and boost recruiter engagement.

Part-Time Study Routes That Lead to Machine Learning Jobs: Evening Courses, Bootcamps & Online Masters

Machine learning—a subset of artificial intelligence—enables computers to learn from data and improve over time without explicit programming. From predictive maintenance in manufacturing to recommendation engines in e-commerce and diagnostic tools in healthcare, machine learning (ML) underpins many of today’s most innovative applications. In the UK, demand for ML professionals—engineers, data scientists, research scientists and ML operations specialists—is growing rapidly, with roles projected to increase by over 50% in the next five years. However, many aspiring ML practitioners cannot step away from work or personal commitments for full-time study. Thankfully, a rich ecosystem of part-time learning pathways—Evening Courses, Intensive Bootcamps and Flexible Online Master’s Programmes—empowers you to learn machine learning while working. This comprehensive guide examines each route: foundational CPD units, immersive bootcamps, accredited online MSc programmes, funding options, planning strategies and a real-world case study. Whether you’re a software developer branching into ML, a statistician aiming to upskill, or a professional exploring AI-driven innovation, you’ll discover how to build in-demand ML expertise on your own schedule.

The Ultimate Assessment-Centre Survival Guide for Machine Learning Jobs in the UK

Assessment centres for machine learning positions in the UK are designed to reflect the complexity and collaboration required in real-world ML projects. From psychometric assessments and live model-building tasks to group data science challenges and behavioural interviews, recruiters evaluate your statistical understanding, coding skills, communication and teamwork. Whether you specialise in deep learning, reinforcement learning or NLP, this guide offers a step-by-step approach to excel at every stage and secure your next ML role.