AI Platform Engineer

Join a next-generation investment and technology team in New York City as an AI Platform 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 AI 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 MLOps, AI Infrastructure, CI/CD and Data Pipelines Engineering-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 Context Protocols (MCP). You will ensure traceability, observability, and scalability from data ingestion through inference.

Roles and Expectations

• Design, build, and maintain production data pipelines that ingest, transform, and deliver structured and unstructured data to downstream ML workflows.
• Own and extend our Prefect-based orchestration layer, including flow scheduling, error handling, retry logic, and human-in-the-loop (HITL) suspend/resume patterns.
• Build and maintain feature stores, data contracts, and promotion workflows that ensure data quality and traceability from raw ingestion through model consumption.
• Collaborate with data scientists to operationalize experimental workflows into reliable, repeatable pipelines.
• Build and maintain scalable infrastructure for model training, retraining, and inference (batch and real-time), including GPU compute provisioning and container orchestration.
• Implement and manage model serving infrastructure - including containerized endpoints, API gateways, and self-serve deployment frameworks for the data science team.
• Deploy and manage monitoring systems that track model health, data drift, prediction consumption, and pipeline reliability.
• Ensure all deployed systems are highly available, resilient, and well-documented with clear data lineage and runbooks.
• Support the buildout and operationalization of agentic AI workflows, including agent hosting, lifecycle management, and integration with Model Context Protocol (MCP) servers.
• Build shared tooling and infrastructure that enables data scientists to develop, test, and deploy agents with minimal friction.
• Design and implement evaluation frameworks and quality standards for AI agents, including automated benchmarking, regression testing, and production-readiness criteria.
• Ensure observability and reliability across agent execution environments, including logging, tracing, and performance monitoring.
• Deploy, configure, and maintain shared AI platform services (e.g., observability tools, memory layers, evaluation platforms) as containerized workloads on Azure - including end-to-end ownership of networking, access, and connectivity between services.
• Manage cloud infrastructure (Azure) including container registries, managed identities, Key Vault secrets, storage backends, and virtual network configurations.
• Maintain CI/CD pipelines, branch protection policies, and release management workflows across data science repositories.
• Continuously evaluate and adopt tools and technologies that improve platform reliability, developer experience, and team velocity.

Required Skills

• 3+ years of experience in data engineering, MLOps, or ML infrastructure roles - with a clear track record of building and maintaining production data and ML pipelines.
• Strong proficiency in Python and SQL, with hands-on experience building ETL/ELT pipelines and data transformation workflows.
• Experience with workflow orchestration tools (Prefect, Airflow, Dagster, or similar) in production environments.
• Solid understanding of containerization and cloud infrastructure - Docker, Kubernetes, and at least one major cloud provider (Azure preferred).
• Hands-on experience deploying and operating containerized services in cloud environments, including configuring networking, load balancing, and service-to-service connectivity.
• Experience with model serving and deployment patterns (batch inference, real-time APIs, feature stores).
• Familiarity with monitoring and observability tooling for pipelines and deployed models (data drift detection, health metrics, alerting).
• Strong documentation habits and the ability to communicate technical architecture clearly to diverse stakeholders.

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.

Location

• Hybrid - 3 days per week in office
• Manhattan, New York City

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.