Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Ai Engineer / Data Scientist

SGI
City of London
3 weeks ago
Create job alert
Overview

One of our leading Investment Management clients is searching for an AI Engineer/Data Scientist to join their R&D as they look to build next-generation AI applications for research, portfolio construction, and decision support. You’ll prototype with LLMs, multi-agent systems, and vector search to solve real investment challenges.

Responsibilities
  • Design and build AI prototypes with LLMs, agent workflows, and knowledge retrieval.
  • Orchestrate multi-agent systems (LangGraph, LangChain).
  • Develop pipelines for prompt engineering and fine-tuned workflows.
  • Create demos and proof-of-concepts for investment use cases.
  • Collaborate with investment teams to identify high-impact applications.
Requirements
  • Strong Python for AI/ML or automation.
  • Experience with large language models and orchestration frameworks.
  • Cloud (AWS preferred) for AI workloads.
  • Background in software engineering or data science.
  • Strong collaboration and problem-solving skills.
Employment type
  • Contract
Seniority level
  • Not Applicable

Please apply with an up-to-date CV to register your interest


#J-18808-Ljbffr

Related Jobs

View all jobs

AI Engineer / Data Scientist

AI Engineer - Data/MLOps

Engineer: Data Science

Engineer: Data Science

Senior Data Scientist (UK)

Senior Data Engineer

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.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.

Machine Learning Team Structures Explained: Who Does What in a Modern Machine Learning Department

Machine learning is now central to many advanced data-driven products and services across the UK. Whether you work in finance, healthcare, retail, autonomous vehicles, recommendation systems, robotics, or consumer applications, there’s a need for dedicated machine learning teams that can deliver models into production, maintain them, keep them secure, efficient, fair, and aligned with business objectives. If you’re hiring for or applying to ML roles via MachineLearningJobs.co.uk, this article will help you understand what roles are typically present in a mature machine learning department, how they collaborate through project lifecycles, what skills and qualifications UK employers look for, what the career paths and salaries are, current trends and challenges, and how to build an effective ML team.