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

• Arcitect, 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.

Qualifications

• Bachelor's degree in Computer Science, Electrical Engineering, or related field.
• Minimum 7 years of relevant experience in ML Ops, distributed systems, and cloud infrastructure.
• Experience with large-scale AI systems and production-grade ML deployments.
• Proven track record of delivering high-quality, scalable ML systems.

Skills

• Python programming skills.
• Familiarity with TypeScript or Go for platform integration.
• Experience with ML frameworks such as PyTorch, Transformers, vLLM, Llama-factory, Megatron-LM, CUDA / GPU acceleration.
• Experience with containerization and orchestration tools like Docker, Kubernetes, Helm, and autoscaling.
• Understanding of ML lifecycle workflows including training, fine-tuning, evaluation, inference, and model registries.
• Experience with observability tools and techniques for ML systems.
• Experience with automated deployment pipelines and infrastructure-as-code.
• Experience with GPU clusters, scheduling, and distributed training frameworks.

Benefits

• Competitive salary and equity options.
• Sign-on bonus.
• Health, Dental, and Vision benefits.
• 401(k) plan.