Principal ML Ops Engineer

About the role

Pragmatike is hiring on behalf of a fast-growing AI startup recognized as a Top 10 GenAI company by GTM Capital, founded by MIT CSAIL researchers. We are seeking a Staff / Principal ML Ops Engineer to lead the design, implementation, and scaling of the companys ML infrastructure and production AI systems.

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.

Requirements

Deep hands-on experience designing and operating production ML systems at scale (Staff/Principal-level expected). 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 (practical understanding). 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.

Bonus points

• Experience deploying and operating LLMs and generative models in production at enterprise scale.
• Familiarity with DevOps, CI/CD, automated deployment pipelines, and infrastructure-as-code.
• Experience optimizing GPU clusters, scheduling, and distributed training frameworks.
• Prior startup experience or comfort operating with ambiguity and high ownership.
• Experience working with data engineering, feature pipelines, or real-time ML systems.

Benefits

Competitive salary & equity options
Sign-on bonus
Health, Dental, and Vision
401k