MLOps Engineer — Evlo AI, Miami, FL

About The Role

The role owns the infrastructure that makes machine learning work in production - CI/CD pipelines for models, scalable training and serving infrastructure, feature stores, and the observability tooling that catches problems before clients do.

This is a hands-on platform engineering role. The MLOps engineer builds the systems that data scientists and ML engineers deploy through daily, and the quality of that infrastructure directly determines how fast the organization ships models.

Key Responsibilities

• Design and maintain CI/CD pipelines for ML workflows using tools like GitHub Actions, Airflow, or Kubeflow - from data validation through model training, testing, and release

• Build and operate model serving infrastructure on Kubernetes (KServe, Seldon, or custom stacks) with autoscaling, canary deployments, and rollback capabilities

• Implement model monitoring and observability: data drift detection, latency and throughput tracking, and automated alerting via Prometheus, Grafana, or Datadog

• Architect and scale feature stores and data pipelines using Spark, Kafka, and cloud-native tooling (SageMaker, Vertex AI, or Databricks)

• Optimize training workloads for cost and speed - distributed training, GPU utilization, spot instance strategies, and experiment tracking with MLflow or W&B

• Harden ML infrastructure for security and compliance: secrets management, access controls, and reproducible, versioned model artifacts

• Partner with data scientists to reduce friction between experimentation and production, driving standards for packaging, testing, and deployment

What We Are Looking For

• 3–6 years of experience in MLOps, DevOps, or ML platform engineering, with multiple production ML systems shipped and maintained

• Strong Python and software engineering fundamentals; comfortable owning infrastructure-as-code with Terraform or Pulumi

• Deep hands-on experience with Kubernetes and at least one major cloud platform (AWS, GCP, or Azure)

• Production experience with workflow orchestration (Airflow, Kubeflow, Dagster) and experiment tracking (MLflow, W&B)

• Solid grasp of containerization, networking, and GPU-based workloads; experience debugging performance issues across the full training-to-serving path

• Bachelor's degree in Computer Science, Engineering, or equivalent practical experience

• Bonus: Experience with LLM inference infrastructure (vLLM, Triton, batching optimization), real-time feature pipelines, or FinOps for ML cost management