Lead Production ML Architect & Systems Builder

Mimecast Limited; Columbus, Northern (OH, KY); Hybrid; USD 172,000 - 258,000; Full time

Job summary

Mimecast Limited is seeking a Principal Machine Learning Engineer to define and drive ML strategy across our threat-detection products. You will own end-to-end ML systems, from data sourcing to deployment, with a focus on scalable, low-latency inference in production.

You will set architectural direction, mentor engineers, and collaborate with product and platform teams to balance performance, cost, and business impact.

Qualifications

• Deep Python proficiency and PyTorch experience.
• Experience with NLP tooling and embeddings, vector indexes.
• Hands-on production ML on AWS (SageMaker, S3, Lambda, Glue, Kinesis, Bedrock).
• Familiarity with model-serving: TorchServe, Triton, KServe.
• Experience with CUDA workloads in production and GPU utilization.
• Leadership as an IC and mentor across teams.

Responsibilities

• Develop and own ML systems end to end from data to deployment and monitoring.
• Set ML architecture across model design, serving, and related systems.
• Guide production model serving using AWS SageMaker, Triton, KServe, and containerized deployments.
• Establish reproducible ML standards and experiment workflows.
• Improve observability with metrics, tracing, and anomaly detection.
• Plan capacity, scaling, and phased deployments; diagnose incidents.

Skills

Python expert

PyTorch

Hugging Face

NLP tooling

ONNX Runtime

CUDA & GPU

SageMaker

Kubernetes

TorchServe

TensorFlow/ML frameworks

Education

Ph.D. in CS/Statistics/Math

M.S. in a quantitative field

Bachelor’s with demonstrated ML depth

Tools

AWS SageMaker

S3, Athena, Lambda, Glue, Kinesis

Bedrock

Terraform

IAM

Containers

Kubernetes

TorchServe

NVIDIA Triton / KServe

CUDA toolkit

Job description

Mimecast Limited is seeking a Principal Machine Learning Engineer to define and drive ML strategy across our threat-detection products. You will own end-to-end ML systems, from data sourcing to deployment, with a focus on scalable, low-latency inference in production.

You will set architectural direction, mentor engineers, and collaborate with product and platform teams to balance performance, cost, and business impact.