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

Apply Now

Principal Machine Learning Engineer

TWG Global
City of London
3 days ago
Create job alert
Overview

Executive Director / Principal Machine Learning Engineer role at TWG Global (TWG Group Holdings, LLC). The organization drives innovation and business transformation across multiple industries by leveraging data and AI as core assets. The role is embedded in the UK Data Science team and reports to the Head of the UK AI & Data Science team.

Responsibilities
  • Translate data science prototypes into production-ready pilot ML services tailored to business use cases
  • Build lightweight pipelines (feature engineering, model packaging, inference services) that integrate with central platforms and meet immediate delivery needs
  • Champion pragmatic MLOps practices (CI/CD for ML, monitoring, observability) to improve reliability without duplicating central engineering’s enterprise frameworks
  • Partner closely with Data Scientists to operationalize models and collaborate with central engineering to plan handoffs of successful pilots for hardening and scale
  • Apply emerging ML engineering techniques (LLM deployment, RAG, vector databases) to accelerate delivery of applied projects
  • Develop reusable components and lessons learned for firm-wide adoption
  • Ensure ML workflows comply with governance, audit, and regulatory requirements
  • Collaborate with central Engineering, Data, Product, and Security teams to align with firm-wide platforms and standards
  • Provide technical mentorship to ML engineers, raising the bar for applied delivery and model deployment
  • Flex into data science tasks when needed: feature engineering, model experimentation, and analytical insights
Requirements
  • 8+ years of experience designing, building, and deploying ML systems in production
  • Proven track record of leading ML engineering projects from prototype to production delivery
  • Deep expertise in modern ML frameworks (TensorFlow, PyTorch, JAX, Ray, MLflow, Kubeflow)
  • Proficiency in Python and at least one backend language (e.g., Java, Scala, Go, C++)
  • Strong knowledge of cloud ML infrastructure (AWS SageMaker, GCP Vertex AI, Azure ML) and containerized deployments (Kubernetes, Docker)
  • Hands-on experience with ML pipelines, distributed training, and inference scaling
  • Familiarity with monitoring stacks (Prometheus, Grafana, ELK, Datadog)
  • Experience in regulated industries (finance, insurance, healthcare) with compliance and governance needs
  • Strong communication and collaboration skills, with the ability to mentor others and influence technical direction
  • Working knowledge of data science techniques (e.g., supervised/unsupervised ML, model evaluation, causal inference, feature engineering)
  • Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or related field (PhD a plus)
Preferred Experience
  • Experience integrating with Palantir platforms (Foundry, AIP, Ontology) as a user/consumer
  • Practical exposure to LLM and GenAI delivery (fine-tuning, RAG, vector search, inference)
  • Experience optimizing GPU clusters or distributed training workloads
  • Familiarity with graph databases (Neo4j, TigerGraph) in applied ML contexts
Benefits
  • Work at the forefront of AI/ML innovation in life insurance, annuities, and financial services
  • Drive AI transformation for sophisticated financial entities
  • Competitive compensation, benefits, future equity options, and leadership opportunities

This is a hybrid position based in the United Kingdom.

We offer a competitive base pay plus a discretionary bonus as part of the compensation package, in addition to a full range of medical, financial, and/or other benefits.

TWG is an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, religion, gender, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.

Seniority level
  • Director
Employment type
  • Full-time
Job function
  • Industries

Note: This job description has been refined for formatting and clarity. It reflects the responsibilities, qualifications, and benefits of the role without altering original content.


#J-18808-Ljbffr

Related Jobs

View all jobs

Principal Machine Learning Engineer

Principal Machine Learning Engineer

Principal Machine Learning Engineer

Principal Machine Learning Engineer

Principal Machine Learning Engineer

Principal Machine Learning 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.

Why AI Careers in the UK Are Becoming More Multidisciplinary

Artificial intelligence is no longer a single-discipline pursuit. In the UK, employers increasingly want talent that can code and communicate, model and manage risk, experiment and empathise. That shift is reshaping job descriptions, training pathways & career progression. AI is touching regulated sectors, sensitive user journeys & public services — so the work now sits at the crossroads of computer science, law, ethics, psychology, linguistics & design. This isn’t a buzzword-driven change. It’s happening because real systems are deployed in the wild where people have rights, needs, habits & constraints. As models move from lab demos to products that diagnose, advise, detect fraud, personalise education or generate media, teams must align performance with accountability, safety & usability. The UK’s maturing AI ecosystem — from startups to FTSE 100s, consultancies, the public sector & universities — is responding by hiring multidisciplinary teams who can anticipate social impact as confidently as they ship features. Below, we unpack the forces behind this change, spotlight five disciplines now fused with AI roles, show what it means for UK job-seekers & employers, and map practical steps to future-proof your CV.

AI Team Structures Explained: Who Does What in a Modern AI Department

Artificial Intelligence (AI) and Machine Learning (ML) are no longer confined to research labs and tech giants. In the UK, organisations from healthcare and finance to retail and logistics are adopting AI to solve problems, automate processes, and create new products. With this growth comes the need for well-structured teams. But what does an AI department actually look like? Who does what? And how do all the moving parts come together to deliver business value? In this guide, we’ll explain modern AI team structures, break down the responsibilities of each role, explore how teams differ in startups versus enterprises, and highlight what UK employers are looking for. Whether you’re an applicant or an employer, this article will help you understand the anatomy of a successful AI department.

Why the UK Could Be the World’s Next AI Jobs Hub

Artificial Intelligence (AI) has rapidly moved from research labs into boardrooms, classrooms, hospitals, and homes. It is already reshaping economies and transforming industries at a scale comparable to the industrial revolution or the rise of the internet. Around the world, countries are competing fiercely to lead in AI innovation and reap its economic, social, and strategic benefits. The United Kingdom is uniquely positioned in this race. With a rich heritage in computing, world-class universities, forward-thinking government policy, and a growing ecosystem of startups and enterprises, the UK has many of the elements needed to become the world’s next AI hub. Yet competition is intense, particularly from the United States and China. Success will depend on how effectively the UK can scale its strengths, close its gaps, and seize opportunities in the years ahead. This article explores why the UK could be the world’s next global hub for artificial intelligence, what challenges it must overcome, and what this means for businesses, researchers, and job seekers.