Machine Learning Engineer

Bracebridge Capital is seeking a full-stack AI/ML Engineer to join the Machine Learning team who will lead and support critical projects aimed at enhancing productivity, automating internal workflows, and deploying machine learning systems at scale. This individual will work on highly-visible initiatives such as managing the Founder’s e-comms workflows and also serve as a bridge between the machine learning team and our IT and software teams. The ideal candidate is deeply technical, highly autonomous, and pragmatic about building modern AI tools in a highly secure, non-cloud environment.

Responsibilities
• Design and maintain on-prem NLP and ML tools, including a next gen communications integration system, which can automatically triage, prioritize, summarize, and route the Founder’s high-volume of electronic communications
• Build intelligent agents to suggest and draft email replies, escalate urgent messages, and integrate with Slack and tasking systems.
• Serve as the technical liaison between the ML team, IT and software development teams.
• Build and manage web portals, dashboards, API interfaces, servers and databases that expose ML outputs, alerts, and data summaries to non-technical users.
• Document workflows and integrate tools into existing operational systems with clear interfaces and security controls.
• Own and operate CI/CD pipelines and containerized environments (Docker, Kubernetes optional) for ML systems and dashboards. Enforce engineering best practices across the ML development lifecycle: version control, testing, model validation, monitoring.
• Monitor and maintain ML infrastructure and ensure smooth operation of GPU/CPU servers, model runtimes, databases, job schedulers, logs and proactively identify and resolve system issues before they impact users.
Qualifications
• Bachelor's or Master’s degree in Computer Science, Machine Learning, Applied Math, or related field
• Minimum of 3 years of professional experience in ML engineering or applied NLP, ideally in a high-stakes, low-latency environment
• Proficiency in Python and modern ML tooling (e.g., PyTorch, spaCy, Hugging Face, LangChain)
• Solid backend engineering experience (e.g., FastAPI, Flask, REST APIs, WebSockets)
• Experience with DevOps: CI/CD (e.g., Azure DevOps, GitHub Actions, Jenkins), Docker, version control
• Experience managing on-prem infrastructure, including job orchestration, storage, and process scheduling
• Familiarity with email parsing (e.g., Outlook APIs, IMAP), document summarization, classification, and routing logic
• Strong communications skills and an eagerness to collaborate across multiple teams within the firm
• Ability to successfully work error free in a fast paced environment with strong understanding of prioritization.