Senior ML Ops Architect – Remote & Scale Production AI

Pragmatike; Adelaide; Remote; AUD 262,000 - 378,000; Full time

Benefits offered by this job

Competitive salary & equity options

Sign-on bonus

Health, Dental, and Vision

401k

Job summary

Pragmatike is hiring a Staff / Principal ML Ops Engineer to lead the design, implementation, and scaling of their ML infrastructure and production AI systems in Cambridge, MA. This high-impact role spans training, deployment, observability, and continuous optimization.

You will collaborate with AI researchers and backend teams to build robust, automated pipelines across cloud and on‑prem GPU clusters, mentor engineers, and drive architectural direction for the AI platform.

Qualifications

• Deep hands-on experience designing and operating production ML systems at scale.
• Strong background in ML Ops, distributed systems, and cloud infrastructure (AWS, GCP, or Azure).
• Proficiency with Python and familiarity with TypeScript or Go for platform integration.
• Expertise in ML frameworks: PyTorch, Transformers, vLLM, Llama-factory, Megatron-LM, CUDA / GPU acceleration.
• Strong experience with containerization and orchestration (Docker, Kubernetes, Helm, autoscaling).
• Deep understanding of ML lifecycle workflows: training, fine-tuning, evaluation, inference, model registries.
• Ability to lead technical strategy, collaborate cross-functionally, and operate in fast-paced environments.

Responsibilities

• Architect, build, and scale the end-to-end ML Ops pipeline, including training, fine-tuning, evaluation, rollout, and monitoring.
• Design reliable infrastructure for model deployment, versioning, reproducibility, and orchestration across cloud and on-prem GPU clusters.
• Optimize compute usage across distributed systems (Kubernetes, autoscaling, caching, GPU allocation, checkpointing workflows).
• Lead the implementation of observability for ML systems (monitor drift, performance, throughput, reliability, cost).
• Build automated workflows for dataset curation, labeling, feature pipelines, evaluation, and CI/CD for ML models.
• Collaborate with researchers to productionize models and accelerate training/inference pipelines.
• Establish ML Ops best practices, internal standards, and cross-team tooling.
• Mentor engineers and influence architectural direction across the entire AI platform.

Skills

ML Ops

Distributed systems

Python

TypeScript or Go

Kubernetes

Docker

CUDA/GPU acceleration

Tools

PyTorch

Transformers

vLLM

Llama-factory

Megatron-LM

CI/CD pipelines

Docker

Kubernetes

Helm

Job description

Pragmatike is hiring a Staff / Principal ML Ops Engineer to lead the design, implementation, and scaling of their ML infrastructure and production AI systems in Cambridge, MA. This high-impact role spans training, deployment, observability, and continuous optimization.

You will collaborate with AI researchers and backend teams to build robust, automated pipelines across cloud and on‑prem GPU clusters, mentor engineers, and drive architectural direction for the AI platform.