Machine Learning Engineer

About Anthelion

Anthelion is a next generation credit investment firm building a proprietary AI and data platform that powers our investment lifecycle from underwriting to portfolio management. The platform integrates structured and unstructured data, advanced analytics, and automated workflows to drive superior, risk adjusted returns in private credit and structured finance.

We are engineers and investors working together to redefine how institutional credit decisions are made, faster, smarter, and more transparent.

The Role

We’re seeking a Machine Learning Engineer responsible for architecting, building, and maintaining robust pipelines for the deployment of ML and AI models, supporting investment process and multi-asset trading strategies. You will develop and maintain end-to-end systems that ensure models are deployed efficiently, consumed reliably, and consistently deliver value to the business.

This role sits at the intersection of data engineering, MLOps, and AI systems infrastructure. The engineer will play a critical role in operationalizing advanced machine learning and AI workflows - ensuring traceability, observability, and scalability from ingestion through inference.

You will also help spearhead the buildout of next-generation agentic workflows, integrating Model Control Platforms (MCP) and managing the lifecycle of AI agents across production environments.

What You’ll Do

Design, build, and maintain scalable ML/AI pipelines for both model retraining and live or batch inference, ensuring reliability, transparency, and traceability throughout the pipeline lifecycle.

Develop and implement monitoring solutions to track model health, including systems for detecting data drift, monitoring model consumption, and assessing performance degradation over time.

Collaborate closely with data scientists and investment team to enable efficient workflows, including the creation and maintenance of feature stores, data pipelines, and model inferencing tools.

Ensure that all deployed pipelines are highly available, scalable, and resilient against failures, supporting both real-time and offline use cases according to business requirements.

Take ownership of key infrastructure that supports the data science team, including data pipelines, scalable virtual machines, storage solutions, automated retraining pipelines, and self-serve model deployment frameworks.

Document all pipeline processes, data lineage, and usage protocols to provide full transparency and facilitate efficient troubleshooting, auditing, and knowledge sharing.

Continuously optimize system performance and resource utilization, implement best practices for deployment, and evaluate new tools and technologies that improve team productivity and reliability.

Spearhead the buildout of advanced agentic workflows, integrating Model Control Platforms (MCP), orchestrating the deployment and management of AI agents, and ensuring robust agent hosting environments.

Ensure observability, reliability, and transparency across all ML/AI pipeline components.

Support broader data pipeline buildout and integration efforts

What We’re Looking For

Proficiency in developing and managing complex ML/AI deployment pipelines using modern orchestration tools.

Experience with large-scale data systems, distributed storage, and cloud infrastructure (e.g., scalable VMs, feature stores, bulk data access solutions).

Strong background in model monitoring, especially systems for data drift, prediction consumption, and pipeline health metrics.

Solid understanding of both batch and real-time inference workflows, including the integration of ML models into production-grade APIs and services.

Excellent documentation skills and the ability to communicate technical concepts to diverse audiences.

Passion for building resilient, transparent, and scalable systems that empower data-driven decision making.

Skills

ML Models

Familiar with common ML models and their deployment workflows.

Familiar with model servicing products (e.g., SageMaker, Vertex AI, BentoML, etc.).

Experience building out pipelines for live and batch model inferencing.

Skilled in managing large numbers of pipelines and implementing monitoring to track health, data flow, and prediction accuracy.

AI Models

Familiar with open-source LLM models Experience with tools that host and serve large-scale models as live or batch endpoints.

Experience working with pipelines that fine-tune and evaluate open-source LLMs in production.

Familiar with agentic workflow frameworks

Experience building and deploying agentic pipelines for production use.

Experience monitoring and tracking agent performance and reliability.

Infrastructure

Familiar with data pipeline tools and orchestration tools such as dbt, Prefect, snowpipe etc.

Familiar with machine learning servicing tools such as Feature Store, Airflow, Ray, etc.

Comfortable managing large compute environments for intensive model training and experiments.

Familiar with Kubernetes and able to set up and manage required open-source tools.

Why Join Anthelion

Build at the frontier of AI, data, and finance, where models directly shape institutional investment decisions.

Work on greenfield architecture with high autonomy and technical depth

Collaborate with a multidisciplinary team of data scientists, engineers, and investors.

Culture grounded in technical excellence, transparency, and measurable impact.

Benefits

Performance-based bonus.

Comprehensive health, dental, and vision insurance.

Retirement savings plan with company match.

Hybrid / flexible work arrangements and a supportive work environment.

Culture:

Demonstrates a strong bias for action and executes quickly with limited guidance

Takes full ownership of outcomes and drives problems to resolution

Approaches challenges with a solutions-first mindset and delivers measurable results

Maintains composure under pressure while keeping momentum and focus

Simplifies complex issues into clear, actionable steps that move the work forward

Salary Range: $140,000 to $200,000 per year