Senior ML Ops Architect — Scale AI Pipelines (Remote)

Pragmatike; New York (NY); Remote; USD 180,000 - 260,000; Full time

Benefits offered by this job

Competitive salary

Equity options

Sign-on bonus

Health, Dental, Vision

401k

Job summary

Pragmatike seeks a Senior/Staff ML Ops leader to architect and scale production ML systems. You will design end-to-end pipelines, deploy on cloud and on-prem GPU clusters, and optimize resources across distributed environments.

You will mentor engineers, drive observability, and define best practices for model lifecycles from training to inference. Remote-friendly with relocation options.

Qualifications

• Proven track record designing and operating production ML systems at scale.
• Strong background in ML Ops, distributed systems, and cloud infrastructure.
• Experience mentoring engineers and shaping architectural direction.

Responsibilities

• Architect, build, and scale end-to-end ML Ops pipelines for training, fine-tuning, evaluation, rollout, and monitoring.
• Design reliable infra for deployment, versioning, reproducibility, and orchestration across cloud and on-prem GPUs.
• Optimize compute usage across Kubernetes clusters, autoscaling, caching, and GPU allocation.
• Lead observability, drift monitoring, performance, throughput, and cost for ML systems.
• Build automated workflows for dataset curation, labeling, feature pipelines, evaluation, and CI/CD.
• Collaborate with researchers to productionize models and accelerate pipelines.
• Establish ML Ops best practices and cross-team tooling; mentor engineers and set direction.

Skills

ML Ops

Distributed systems

Cloud infrastructure

Python

TypeScript or Go

PyTorch

Transformers

Llama-factory

Megatron-LM

CUDA / GPU acceleration

Docker

Kubernetes

Helm

CI/CD

Model registries

Education

BS/MS in CS/math/engineering or equivalent

Tools

AWS

GCP

Azure

Job description

Pragmatike seeks a Senior/Staff ML Ops leader to architect and scale production ML systems. You will design end-to-end pipelines, deploy on cloud and on-prem GPU clusters, and optimize resources across distributed environments.

You will mentor engineers, drive observability, and define best practices for model lifecycles from training to inference. Remote-friendly with relocation options.