About the job
Join TWG Group Holdings, LLC, a leader in driving innovation and business transformation across diverse sectors including financial services, insurance, technology, media, and sports. We harness the power of data and AI as fundamental assets, adopting an AI-first, cloud-native approach that delivers real-time intelligence and interactive applications. This empowers both our customers and employees to make informed decisions.
We are committed to ethical data and AI practices, ensuring compliance with regulatory standards. Our decentralized structure supports autonomous business units, while a central AI Solutions Group collaborates with top data and AI vendors to drive transformative marketing, operational, and product development initiatives.
In this role, you will work closely with management to propel our data and analytics transformation, enhancing productivity and fostering agile, data-driven decisions. By leveraging partnerships with leading tech startups and academic institutions, you will contribute to developing competitive advantages and driving enterprise innovation.
Your efforts will be crucial in supporting TWG Global's mission of sustained growth and creating significant value across our organization.
The Role
As the Staff Machine Learning Engineer (VP) within the ML Engineering team, you will be instrumental in designing, deploying, and scaling advanced ML systems that underpin core business functions. Reporting directly to the Executive Director of ML Engineering, your primary responsibilities will include building production-grade ML infrastructure, reusable frameworks, and scalable model pipelines that yield measurable outcomes—from cost optimization to revenue growth. You will act as a technical thought leader, influencing the organization’s machine learning practices and promoting a culture of operational excellence, reliability, and responsible AI adoption.
Key Responsibilities:
- Design and implement ML systems and platforms that address significant business challenges within regulated environments.
- Lead the creation of production-ready pipelines, including feature stores, model registries, and scalable inference services.
- Advocate for MLOps best practices (CI/CD for ML, model versioning, monitoring, observability) to ensure model reliability and cost-effectiveness.
- Collaborate with Data Scientists to operationalize experimental models, facilitating scalability across various business domains.
- Incorporate emerging ML engineering techniques (e.g., LLM deployment, fine-tuning pipelines, vector databases, RAG systems) into enterprise solutions.
- Design foundational ML platforms and frameworks that serve as essential components for downstream AI applications.
- Implement governance, auditability, and control measures into ML systems to ensure ethical and responsible use.