Principal Machine Learning Engineer

At TWG Group Holdings, LLC (“TWG Global”), we drive innovation and business transformation across a range of industries, including financial services, insurance, technology, media, and sports, by leveraging data and AI as core assets. Our AI-first, cloud-native approach delivers real-time intelligence and interactive business applications, empowering informed decision-making for both customers and employees. 

We prioritize responsible data and AI practices, ensuring ethical standards and regulatory compliance. Our decentralized structure enables each business unit to operate autonomously, supported by a central AI Solutions Group, while strategic partnerships with leading data and AI vendors fuel game-changing efforts in marketing, operations, and product development. 

You will collaborate with management to advance our data and analytics transformation, enhance productivity, and enable agile, data-driven decisions. By leveraging relationships with top tech startups and universities, you will help create competitive advantages and drive enterprise innovation. 

At TWG Global, your contributions will support our goal of sustained growth and superior returns, as we deliver rare value and impact across our businesses. 

The Role:

As a Principal Machine Learning Engineer, you will be embedded in our Data Science team and play a critical role in accelerating the delivery of AI solutions. Reporting to the Head of the UK AI & Data Science team, you will work alongside Data Scientists to take prototypes and translate them into reliable, production-ready services for business stakeholders.

This is a hands-on technical leadership role: you will set direction, architect solutions, and mentor peers. The remit is squarely focused on last-mile delivery — taking prototypes built by Data Scientists and making them usable in real business settings by packaging them into services or APIs, wiring them into data sources, building lightweight feature and inference pipelines, and adding basic monitoring and retraining logic. You will accelerate pilot deployments so stakeholders can see value quickly, and then hand the solution off to the Central Engineering and Data team, who are responsible for firm-wide platforms, scaling, and long-term support.

The ideal candidate will also bring applied data science skills and be comfortable moving between ML engineering and data science work. You should be able to contribute to model development and analysis when needed, in addition to owning deployment and operationalization.

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 smoothly with central platforms while meeting 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 that central teams can adopt into firm-wide platforms.
• Ensure ML workflows comply with governance, audit, and regulatory requirements.
• Collaborate with central Engineering, Data, Product, and Security teams to ensure alignment 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, reflecting the versatility required in a fast-moving team.

• 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 a related technical 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.

This is a hybrid position based in New York, NY or Santa Monica, CA. Fully remote candidates will not be considered.

We offer a competitive base pay + a discretionary bonus will be provided 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.