ML Platform Engineer

Foundry AI Partners; Northern (KY); On-site; USD 120,000 - 160,000; Full time

Job summary

Foundry AI Partners seeks a Platform Engineer to own and scale the ML infrastructure powering model deployment and serving. You will craft scalable platforms, implement robust monitoring, and optimize end-to-end workflows across client environments.

The role emphasizes building CI/CD for training and deployment, reducing latency, and managing costs on GPU/CPU workloads while ensuring security and governance for enterprise clients.

Qualifications

• 4+ years of experience in platform engineering or DevOps.
• Strong knowledge of Kubernetes, Docker, and cloud infrastructure (AWS/GCP/Azure).
• Experience with ML model serving frameworks (TorchServe, TensorFlow Serving, etc.).
• Proficiency in Python and infrastructure-as-code tools (Terraform, Pulumi).
• Understanding of ML workflows: training, evaluation, deployment, monitoring.
• Excellent debugging skills and operational mindset.

Responsibilities

• Design and maintain ML infrastructure for model deployment and serving
• Build monitoring and observability systems for production AI agents
• Implement CI/CD pipelines for model training, evaluation, and deployment
• Optimize inference latency and cost across GPU/CPU workloads
• Develop tooling for experiment tracking, versioning, and reproducibility
• Ensure security, compliance, and data governance for enterprise clients

Skills

Platform engineering

DevOps

Kubernetes

Python

Cloud infrastructure

ML model serving

Education

Bachelor's degree or equivalent in CS/Math/Engineering

Tools

Kubernetes

Docker

Terraform

Pulumi

TorchServe

TensorFlow Serving

Job description

Own the infrastructure that powers our AI deployments. Build scalable platforms for model serving, monitoring, and continuous improvement across client environments.

Responsibilities

• Design and maintain ML infrastructure for model deployment and serving
• Build monitoring and observability systems for production AI agents
• Implement CI/CD pipelines for model training, evaluation, and deployment
• Optimize inference latency and cost across GPU/CPU workloads
• Develop tooling for experiment tracking, versioning, and reproducibility
• Ensure security, compliance, and data governance for enterprise clients

Qualifications

• 4+ years of experience in platform engineering or DevOps
• Strong knowledge of Kubernetes, Docker, and cloud infrastructure (AWS/GCP/Azure)
• Experience with ML model serving frameworks (TorchServe, TensorFlow Serving, etc.)
• Proficiency in Python and infrastructure-as-code tools (Terraform, Pulumi)
• Understanding of ML workflows: training, evaluation, deployment, monitoring
• Excellent debugging skills and operational mindset

Nice to Have

• Experience with GPU orchestration and optimization
• Knowledge of vector databases (Pinecone, Weaviate, Qdrant)
• Familiarity with MLOps tools (MLflow, Weights & Biases, Kubeflow)
• Background in distributed systems or database internals
• Contributions to open-source infrastructure projects