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.