Infrastructure Engineer
Purpose
The firm is seeking an Infrastructure Engineer to build and operate the foundational systems that power their data, analytics, and AI platform. This is an infrastructure and DevOps role, focusing on cloud infrastructure, deployment pipelines, orchestration, networking, and observability.
Roles and Expectations
- Deploy, configure, and maintain shared platform services as containerized workloads including end-to-end ownership of networking, access, and connectivity between services.
- Manage cloud infrastructure, including container registries, managed identities, Key Vault secrets, storage backends, and virtual network configurations.
- Build and maintain CI/CD pipelines, branch protection policies, and release management workflows across repositories.
- Continuously evaluate and adopt tools and technologies that improve platform reliability, developer experience, and team velocity.
Required Skills
- 3+ years of experience in infrastructure, DevOps, platform engineering, or SRE roles with a clear track record of building and maintaining production systems.
- Strong understanding of containerization and cloud infrastructure – Docker, Kubernetes, and at least one major cloud provider.
- Hands-on experience deploying and operating containerized services in cloud environments, including configuring networking, load balancing, and service-to-service connectivity.
- Experience building and maintaining CI/CD pipelines, Git-based release management, and branch protection workflows.
- Experience with workflow orchestration tools (Prefect, Airflow, Dagster, or similar) in production environments.
- Familiarity with monitoring and observability tooling (health metrics, alerting, logging, and tracing).
- Strong documentation habits and the ability to communicate technical architecture clearly to diverse stakeholders.
Nice to Have
- A genuine interest in AI and a desire to learn and grow into building, hosting, and operating AI agents and agentic systems.
- Familiarity with agentic workflow frameworks (e.g., MCP, LangChain, or similar).
- Experience with MLOps or ML infrastructure, including model training, retraining, and inference workflows.
- Familiarity with model serving and deployment patterns (batch inference, real-time APIs, feature stores).
- Experience standing up and maintaining third-party AI/ML platform tools (e.g., Langfuse, MLflow, or similar observability and evaluation platforms).
- Experience managing internal Python package distribution (private PyPI, Artifactory, or similar).
- Openness to flexing into adjacent engineering work (data engineering, software engineering, etc.) to help fill extra capacity where the team needs it.
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.
About QDStaff
QDStaff is North America's leading talent source for award-winning video game companies. We rotate job postings on platforms which sometimes causes jobs to appear closed when they are removed. Consult our careers page at https://www.qdstaff.com/careers to get access to this role and similar great opportunities. Follow us on LinkedIn https://www.linkedin.com/company/qdstaff/. Visit us at https://www.qdstaff.com.