Principal Machine Learning Engineer

TWG Global AI • New York, NY

75d

About The Position

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.

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 a related technical field (PhD a plus).

Nice To Haves

• 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.

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.

Benefits

• Competitive base pay
• Discretionary bonus
• Full range of medical benefits
• Financial benefits

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Master's degree