Machine Learning Engineer

Join a next-generation investment and technology team in New York City as a Machine Learning Engineer. This firm is building a proprietary AI and data platform to power an end-to-end investment lifecycle. The role involves architecting, building, and maintaining robust ML/AI pipelines, supporting investment research, underwriting, and multi-asset trading strategies. You will operationalize advanced machine learning and AI workflows, ensuring models are deployed efficiently, consumed reliably, and monitored with full transparency.

Must Have

• Design, build, and maintain scalable ML/AI pipelines
• Develop and implement monitoring systems for model health
• Collaborate with data scientists and investment teams
• Ensure high availability and scalability for deployed models
• Own infrastructure for the data science ecosystem
• Optimize system performance and implement MLOps best practices
• Spearhead advanced agentic workflows
• Master’s degree required
• 3-5 years experience as ML/MLOps Engineer
• Proficiency with complex ML/AI deployment pipelines
• Background in large-scale data systems and cloud infrastructure
• Strong experience monitoring model health and data drift
• Solid understanding of batch and real-time inference pipelines

Perks & Benefits

• Performance-based bonus
• Comprehensive health, dental, and vision insurance
• Retirement savings plan with company match
• Hybrid work structure with flexibility and strong team support

Join a next-generation investment and technology team in New York City as a Machine Learning Engineer. This firm is building a proprietary AI and data platform that powers an end-to-end investment lifecycle integrating structured and unstructured data, advanced analytics, and automated workflows to drive superior, risk-adjusted performance. Their multidisciplinary team of engineers and investors is redefining how institutional-grade decisions are made across private credit and structured finance.

Purpose

They are seeking an experienced Machine Learning Engineer to architect, build, and maintain robust ML/AI pipelines that support investment research, underwriting, and multi-asset trading strategies. The ideal team member brings deep expertise in data engineering, MLOps, and AI systems infrastructure ensuring that models are deployed efficiently, consumed reliably, and monitored with full transparency.

This role will help operationalize advanced machine learning and AI workflows across the firm, including next-generation agentic systems built on Model Control Platforms (MCP). You will ensure traceability, observability, and scalability from data ingestion through inference.

Roles and Expectations

• Design, build, and maintain scalable ML/AI pipelines for retraining and live or batch inference ensuring reliability, transparency, and full traceability.
• Develop and implement monitoring systems that track model health, detect data drift, and assess prediction performance over time.
• Collaborate closely with data scientists and investment teams to create efficient workflows, including feature stores, data pipelines, and inference tools.
• Ensure high availability, scalability, and resilience for all deployed models across real-time and offline use cases.
• Own infrastructure that powers the data science ecosystem, including compute environments, storage layers, automated retraining systems, and self-serve deployment frameworks.
• Document pipelines, data lineage, and usage protocols to support auditing, troubleshooting, and knowledge sharing.
• Optimize system performance, evaluate new tools/technologies, and implement MLOps best practices.
• Spearhead development of advanced agentic workflows integrating MCPs, orchestrating AI agent lifecycles, and maintaining reliable hosting environments.
• Ensure end-to-end observability and transparency across all ML/AI system components.
• Support broader data pipeline buildout and integration initiatives.

Qualifications / Education

• Master’s degree required.
• 3–5 years of experience as an ML Engineer, MLOps Engineer, or similar role.
• Proficiency developing and managing complex ML/AI deployment pipelines using modern orchestration tools.
• Background with large-scale data systems, distributed storage, and cloud infrastructure.
• Strong experience monitoring model health, data drift, and consumption metrics.
• Solid understanding of both batch and real-time inference pipelines, including production-grade APIs.
• Excellent documentation and communication skills.
• Passion for designing resilient, scalable, and transparent systems that elevate data-driven decision making.

Required Skills

• Familiarity with common ML models and deployment workflows.
• Experience with model-serving platforms such as SageMaker, Vertex AI, BentoML, etc.
• Ability to build pipelines for live and batch inference at scale.
• Skilled in managing large numbers of pipelines and implementing monitoring for data flow, health, and accuracy.
• Experience with open-source LLMs and tools for hosting/serving large models.
• Experience fine-tuning and evaluating LLMs in production environments.
• Familiarity with agentic workflow frameworks.
• Experience building, deploying, and monitoring agentic pipelines and AI agents.
• Familiarity with data pipeline and orchestration tools (dbt, Prefect, Snowpipe, etc.).
• Knowledge of ML servicing tools such as Feature Store, Airflow, Ray, etc.
• Comfortable managing large compute environments for intensive modeling and experimentation.
• Familiarity with Kubernetes and associated open-source tools.

Benefits

• Performance-based bonus.
• Comprehensive health, dental, and vision insurance.
• Retirement savings plan with company match.
• Hybrid work structure with flexibility and strong team support.

About the Company

Join a team that blends deep technical expertise with institutional-level investing. This firm is building an advanced AI and data platform that powers the full investment lifecycle, enabling faster, smarter, and more transparent decision-making. Their approach combines engineering precision with financial insight delivering systems that integrate diverse datasets, advanced analytics, and automated workflows. They value ownership, clarity, and innovation, and they are building a high-performance environment where technical talent can have direct impact on real-world investment outcomes.