ML Ops Engineer - Real-Time Model Deployment

Bana Solutions; Chantilly (VA); On-site; USD 150,000 - 210,000; Full time

Job summary

Bana Solutions in Chantilly, VA, seeks an ML Ops Engineer to own the machine-learning lifecycle from trained artifacts to live, low-latency serving. You will monitor models, manage automatic retraining, and ensure governance and reproducibility across experiments.

Responsibilities include packaging and optimizing PyTorch/TensorFlow models, deploying via Kubernetes/Docker, and maintaining model lineage with MLflow and Evidently. Experience with Feast and model monitoring is required.

Qualifications

• End-to-end ML lifecycle ownership from trained artifact to live serving.
• Experience with MLflow or equivalent for versioning and lineage.
• Real-time model serving with low latency.
• Drift monitoring with Evidently or similar.
• Automated retraining pipelines via Airflow on GPUs.
• PyTorch or TensorFlow in production; optimization.
• Feature store usage with Feast.
• Kubernetes and Docker for deployment.

Responsibilities

• Own the ML lifecycle from trained artifact to live serving.
• Manage model release, versioning, and rollback.
• Serve models for real-time inference with low latency.
• Monitor models and predictions; detect drift.
• Automate retraining pipelines with gates and promotions.
• Ensure reproducibility and governance of experiments.
• Coordinate feature store usage to prevent training-serving skew.

Skills

ML lifecycle ownership

Python

Kubernetes

Docker

Airflow

Model monitoring

Git

Tools

MLflow

TorchServe

Triton

Seldon

Feast

Prometheus

Grafana

S3/MinIO

Job description

Bana Solutions in Chantilly, VA, seeks an ML Ops Engineer to own the machine-learning lifecycle from trained artifacts to live, low-latency serving. You will monitor models, manage automatic retraining, and ensure governance and reproducibility across experiments.

Responsibilities include packaging and optimizing PyTorch/TensorFlow models, deploying via Kubernetes/Docker, and maintaining model lineage with MLflow and Evidently. Experience with Feast and model monitoring is required.