Macpower Digital Assets Edge
Rust AI Platform Engineering Director
Macpower Digital Assets Edge, New York, New York, us, 10261
We are looking for highly motivated and determined engineers to set and drive a clear AI Software Strategy, delivering cohesive AI experiences across the firm. You will have the opportunity to work with some of the brightest minds in the industry, leveraging your insights and expertise to advance AI Platform Engineering. We value diversity and believe it is the key to our success, ensuring that your unique skills, curiosity, and passion are nurtured. Join us and grow both technically and personally while working at one of the most recognized financial companies in the world as part of an AI software development team.
Key Responsibilities:
Design, develop, and maintain the next generation of scalable AI platform for the world's best investment management technology platform. Implement and manage
Kubernetes
clusters for deploying
AI models . Build platform abstractions to manage cloud-native infrastructure across
AWS, GCP, or Azure
environments. Build and maintain automated pipelines for continuous training, testing, and deployment of machine learning models, with integrated enterprise concerns. Ensure the security and compliance of the platform. Troubleshoot and resolve issues related to platform performance and reliability. Refine business and functional requirements and translate them into scalable technical designs. pply quality software engineering practices throughout the software development lifecycle. Work with team members in a multi-office, multi-country environment. Stay updated with the latest trends and technologies in AI and cloud engineering. Requirements:
B.S./M.S. degree in Computer Science, Engineering, or a related subject area. 10+ years of experience in software and platform engineering. Proficiency in designing and building scalable APIs and microservices. Strong proficiency in Kubernetes, including Helm charts, Kustomize, and custom resource definitions (CRDs). Hands-on experience with cloud platforms such as
AWS, GCP, or Azure . Expertise in containerization technologies (Docker, containerd). Experience in
CI/CD tools
(Jenkins, GitHub Actions, ArgoCD). Knowledge of
infrastructure such as code (IaC)
tools like Terraform or CloudFormation. Solid understanding of networking concepts, security policies, and API gateways in cloud environments. Proficiency in production-grade programming languages such as
Rust, K8's, Cloud Infra, CI/CD Tools, API, Microservices and C++. Decent understanding of distributed systems, cluster orchestration and management. Good knowledge of
data science tools
(e.g
PyTorch, Jax, Numpy ) and programming languages such as Python. Experience with
monitoring tools (Prometheus, Grafana). Experience working in Agile development teams with excellent collaboration skills. Grit in the face of technical obstacles. Nice to have:
Building
SDK Documentation, AI Infra
and client libraries to support API consumption. Knowledge of distributed data processing frameworks (
Spark, Dask ). Understanding of GPU orchestration and optimization in
Kubernetes . Familiarity with
MLOps
and
ML Model
lifecycle pipelines. Experience with AI model training and fine-tuning. Familiarity with event-driven architecture and messaging frameworks like
Kafka . Experience with NoSQL datastores like
Cassandra .
Design, develop, and maintain the next generation of scalable AI platform for the world's best investment management technology platform. Implement and manage
Kubernetes
clusters for deploying
AI models . Build platform abstractions to manage cloud-native infrastructure across
AWS, GCP, or Azure
environments. Build and maintain automated pipelines for continuous training, testing, and deployment of machine learning models, with integrated enterprise concerns. Ensure the security and compliance of the platform. Troubleshoot and resolve issues related to platform performance and reliability. Refine business and functional requirements and translate them into scalable technical designs. pply quality software engineering practices throughout the software development lifecycle. Work with team members in a multi-office, multi-country environment. Stay updated with the latest trends and technologies in AI and cloud engineering. Requirements:
B.S./M.S. degree in Computer Science, Engineering, or a related subject area. 10+ years of experience in software and platform engineering. Proficiency in designing and building scalable APIs and microservices. Strong proficiency in Kubernetes, including Helm charts, Kustomize, and custom resource definitions (CRDs). Hands-on experience with cloud platforms such as
AWS, GCP, or Azure . Expertise in containerization technologies (Docker, containerd). Experience in
CI/CD tools
(Jenkins, GitHub Actions, ArgoCD). Knowledge of
infrastructure such as code (IaC)
tools like Terraform or CloudFormation. Solid understanding of networking concepts, security policies, and API gateways in cloud environments. Proficiency in production-grade programming languages such as
Rust, K8's, Cloud Infra, CI/CD Tools, API, Microservices and C++. Decent understanding of distributed systems, cluster orchestration and management. Good knowledge of
data science tools
(e.g
PyTorch, Jax, Numpy ) and programming languages such as Python. Experience with
monitoring tools (Prometheus, Grafana). Experience working in Agile development teams with excellent collaboration skills. Grit in the face of technical obstacles. Nice to have:
Building
SDK Documentation, AI Infra
and client libraries to support API consumption. Knowledge of distributed data processing frameworks (
Spark, Dask ). Understanding of GPU orchestration and optimization in
Kubernetes . Familiarity with
MLOps
and
ML Model
lifecycle pipelines. Experience with AI model training and fine-tuning. Familiarity with event-driven architecture and messaging frameworks like
Kafka . Experience with NoSQL datastores like
Cassandra .