Senior Machine Learning Engineer; NYC

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.

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.

As a Senior Associate, Machine Learning Engineer, you'll work alongside experienced ML engineers and data scientists to design, build, and scale machine learning systems that deliver real business value. Reporting to the Executive Director of ML Engineering, you'll gain hands‑on experience developing production‑grade pipelines, monitoring frameworks, and scalable ML applications that support mission‑critical business functions. This is a high‑growth opportunity for someone with early industry experience (or strong academic grounding) in machine learning engineering, eager to deepen their expertise in production systems and MLOps while growing within a dynamic AI team operating at the frontier of applied ML.

• Contribute to the design, development, and deployment of ML models and pipelines across business‑critical domains such as financial services and insurance.
• Support production efforts, including model packaging, integration, CI/CD deployment, and monitoring for performance, drift, and reliability.
• Collaborate with senior engineers to build internal ML engineering tools and infrastructure that improve training, testing, and observability workflows.
• Partner with Data Scientists to operationalize prototype models, ensuring they are scalable, robust, and cost‑efficient in production.
• Work with large‑scale datasets to enable feature engineering, transformation, and quality assurance within ML pipelines.
• Implement monitoring dashboards, alerts, and diagnostics for model health and system performance.
• Contribute to documentation, governance, and reproducibility practices, supporting compliance in regulated environments.

• 5+ years of experience building and deploying machine learning models in production environments, with exposure to monitoring and diagnostics.
• Solid understanding of machine learning engineering fundamentals (pipelines, deployment, monitoring) and familiarity with data science workflows.
• Experience with MLOps tools such as MLflow, Weights & Biases, or equivalent. Exposure to observability/monitoring systems (Prometheus, Grafana, ELK, Datadog) is a plus.
• Proficiency in Python and familiarity with ML libraries (scikit‑learn, XGBoost, Tensor Flow, PyTorch).
• Strong experience with data manipulation and pipelines using Pandas, Num Py, and SQL.
• Knowledge of containerized deployments (Docker, Kubernetes) and cloud ML services (AWS Sage Maker, GCP Vertex AI, or Azure ML) preferred.
• Excellent problem‑solving skills, eagerness to learn, and ability to thrive in a fast‑paced, evolving environment.
• Bachelor's or Master's degree in Computer Science, Machine Learning, or a related technical field.
• Strong written and verbal communication skills, with the ability to explain technical details to both technical and business stakeholders.

• Hands‑on experience with Palantir platforms (Foundry, AIP, Ontology), including deploying and integrating ML solutions in enterprise ecosystems.
• Familiarity with vector databases (FAISS, Pinecone, Milvus, Weaviate) and LLM engineering workflows.
• Exposure to…