Overview

Staff Machine Learning Engineer (VP) on the ML Engineering team reporting to the Executive Director of ML Engineering. You will design, deploy, and scale advanced ML systems that power core business functions across the enterprise. Build production-grade ML infrastructure, reusable frameworks, and scalable model pipelines that drive measurable business outcomes, including cost optimization and top-line growth. Act as a technical thought leader, shaping ML engineering practices and fostering a culture of operational excellence, reliability, and responsible AI adoption.

Responsibilities

• Architect and deploy ML systems and platforms that solve high-impact business problems in regulated enterprise environments.
• Lead development of production-ready pipelines, including feature stores, model registries, and scalable inference services.
• Champion MLOps best practices (CI/CD for ML, model versioning, monitoring, observability) for reliable, reproducible, and cost-efficient models.
• Partner with Data Scientists to operationalize experimental models for scalability across diverse business domains.
• Integrate emerging ML engineering techniques (e.g., LLM deployment, fine-tuning pipelines, vector databases, RAG systems) into enterprise-ready solutions.
• Own the design of foundational ML platforms and frameworks serving as building blocks for downstream AI applications.
• Embed controls, governance, and auditability into ML workflows to ensure regulatory compliance and responsible AI.
• Collaborate with Engineering, Product, and Security teams to embed ML-driven decision-making into platforms and workflows.
• Define and track engineering and model performance metrics (latency, scalability, cost, accuracy) to optimize systems in production.
• Mentor ML engineers, fostering technical excellence, collaboration, and innovation within the AI Science team.

Requirements

• 8+ years of experience building and deploying machine learning systems in production environments at enterprise or platform scale.
• Proven track record of leading ML engineering projects from architecture to deployment, including ownership of production-grade systems.
• Deep expertise in ML frameworks and engineering stacks (TensorFlow, PyTorch, JAX, Ray, MLflow, Kubeflow).
• Proficiency in Python and at least one backend language (e.g., Java, Scala, Go, C++).
• Strong understanding of cloud ML infrastructure (AWS SageMaker, GCP Vertex AI, Azure ML) and containerized deployments (Kubernetes, Docker).
• Hands-on experience with data and model pipelines (feature stores, registries, distributed training, inference scaling).
• Knowledge of observability and monitoring stacks (Prometheus, Grafana, ELK, Datadog) for ML system performance.
• Experience collaborating with cross-functional teams in regulated industries (finance, insurance, health) with compliance and governance needs.
• Exceptional communication and leadership skills, with the ability to translate complex engineering challenges into clear business outcomes.
• Master’s or PhD in Computer Science, Machine Learning, or related technical discipline.

Preferred experience

• Hands-on experience with Palantir platforms (Foundry, AIP, Ontology) including developing, deploying, and integrating ML solutions in enterprise ecosystems.
• Exposure to LLM and GenAI engineering (fine-tuning, vector search, distributed inference).
• Experience optimizing GPU clusters, distributed training, or HPC environments.
• Familiarity with graph databases (e.g., Neo4j, TigerGraph) and their application in AI/ML systems.

Benefits

• Work at the forefront of AI/ML innovation in life insurance, annuities, and financial services.
• Drive AI transformation for sophisticated financial entities.
• Competitive base pay, discretionary bonus, and a full range of medical, financial, and/or other benefits.

This is a hybrid position based in the United Kingdom.

We offer a competitive base pay plus a discretionary bonus, along with a full range of 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.