Tektend LLC is seeking a skilled Machine Learning Infrastructure Engineer to join our engineering team on-site in Tampa, FL. In this role, you will design, scale, and optimize the distributed computing and orchestration platforms that power our end-to-end machine learning lifecycle, bridging the gap between data science experimentation and enterprise-grade production systems.
Key Responsibilities:
- Architect, deploy, and operate high-performance compute clusters and Kubernetes environments optimized for distributed ML training and inference.
- Build and automate robust MLOps and CI/CD pipelines enabling seamless model tracking, packaging, artifact registry, and zero-downtime deployment.
- Optimize GPU/CPU hardware acceleration, memory allocation, and networking to lower training latency and cloud infrastructure costs.
- Partner with Data Scientists and ML Researchers to integrate scalable workflows using frameworks such as PyTorch, Ray, and MLflow.
- Implement enterprise-level monitoring, alerting, telemetry, and observability for model health, data drift, and cluster utilization.
Requirements:
- Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or a related quantitative field.
- 3+ years of professional experience focused on ML Infrastructure, MLOps, or Cloud Platform Engineering.
- Proficiency in Python and strong experience with container orchestration platforms (Docker, Kubernetes).
- Hands-on experience with modern cloud environments (AWS or GCP) and Infrastructure-as-Code tooling (Terraform).
- Familiarity with distributed model training paradigms, GPU acceleration (CUDA), and low-latency inference runtimes (e.g., Triton, TorchServe).
- Willingness and ability to work full-time on-site at our Tampa, FL office.
