Principal ML Ops Engineer

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Pragmatike

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2022

🎯 Recruiter

👥 HR Tech

🤝 B2B

Recruitment • HR Tech • B2B

Pragmatike is a remote IT recruitment and staffing company that sources, vets, and places international tech talent for businesses. They provide human-curated matching (not solely AI), technical assessments, and fast placements—claiming qualified specialists can be introduced within 48 hours—while handling onboarding, invoicing and international payroll. Pragmatike serves startups and enterprises with roles like developers, data engineers, mobile and game developers, product managers and specialists, operating across 60+ countries with a large vetted talent pool.

📋 Description

• Build and operate production-grade model serving infrastructure using frameworks such as vLLM, TGI, Triton, or equivalent • Design and implement robust deployment pipelines with blue/green and canary rollout strategies for ML models • Develop and maintain auto-scaling systems, multi-model serving architectures, and intelligent request routing layers • Optimize GPU utilization, memory efficiency, network throughput, and model artifact storage performance • Design observability systems for tracking inference latency, throughput, GPU usage, cost metrics, and system health • Manage model registries and CI/CD pipelines enabling automated and reproducible model deployments • Own the full lifecycle of ML systems from development through production, including operational support and on-call responsibilities • Define engineering best practices and contribute to platform scalability in a fast-moving startup environment

🎯 Requirements

• 4+ years of experience in ML Ops, Platform Engineering, SRE, or similar infrastructure roles focused on ML systems • Hands-on experience with model serving frameworks such as vLLM, TGI, Triton, or equivalent • Strong background in container orchestration and operating GPU-based workloads in production • Experience with MLOps tooling including model registries, experiment tracking, and automated deployment pipelines • Proficiency in Python and infrastructure-as-code tools (e.g., Terraform, Helm, or similar) • Strong understanding of distributed systems, performance tuning, and production reliability engineering • Ability to effectively use AI coding assistants to accelerate development and debugging workflows • Ownership mindset with the ability to operate independently in a remote-first environment.

🏖️ Benefits

• Take ownership of critical infrastructure powering a rapidly scaling AI-native cloud platform • Build foundational ML inference systems from the ground up in a high-growth, well-funded startup • Work at the intersection of distributed systems, GPU computing, and sustainable cloud architecture • Gain deep expertise in next-generation AI infrastructure and large-scale model serving systems • Influence core engineering decisions and define best practices that will scale with the company.