ML Ops Infrastructure Engineer

🕒 Abril 6

🇺🇸 Estados Unidos – Remoto (EUA)

💵 $160.000 - $220.000 / ano

⏰ Tempo Integral

🟡 Pleno

🟠 Sênior

👷 Engenheiro de Infraestrutura

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Deepgram

SiteLinkedInTodas as Vagas

51 - 200 funcionários

Fundada em 2015

💼 Consultoria

🏥 Saúde

📦 Logística

💰 $47.000.000 Series B em 2022-11

Consulting • Healthcare • Logistics

A Deepgram é uma empresa líder em IA de voz que fornece APIs poderosas para aplicações de reconhecimento de fala, síntese de texto para fala e entendimento de linguagem. Sua plataforma permite que os desenvolvedores criem soluções avançadas de IA de voz para casos de uso como centrais de atendimento, transcrição médica, IA conversacional, entre outros. Conhecida por sua precisão inigualável, velocidade e custo-benefício, a tecnologia da Deepgram é confiada por grandes empresas e startups em todo o mundo. Oferecendo capacidades de transcrição em tempo real e altamente precisas, a Deepgram ajuda as empresas a obter insights a partir de dados de voz, tornando-se uma ferramenta essencial para transformar interações de voz.

Descrição

• Design and build CI/CD pipelines specifically tailored for ML model development, validation, and deployment • Architect and maintain model deployment pipelines that move models from research environments through staging to production with confidence • Build A/B testing infrastructure that enables controlled rollouts of new models and measures real-world performance impact • Implement comprehensive monitoring for model performance in production -- accuracy metrics, latency, drift detection, and regression alerts • Develop automated retraining pipelines that trigger on data changes, performance degradation, or scheduled cadences • Create and maintain build and test environments that mirror production, giving researchers high-fidelity feedback before deployment • Establish model versioning, artifact management, and rollback capabilities to ensure safe and reproducible deployments • Collaborate with research engineers to define and enforce model quality gates before production promotion • Build observability dashboards that give the team real-time insight into model health across all environments • Optimize model serving infrastructure for latency, throughput, and cost efficiency

🎯 Requisitos

• 4+ years of experience in MLOps, DevOps, or infrastructure engineering with a focus on ML systems • Strong proficiency in Python and experience building automation and tooling for ML workflows • Deep experience with CI/CD systems and building pipelines for software and model delivery • Hands-on experience with Docker and Kubernetes for containerized workload management • Practical experience deploying and serving ML models in production environments • Familiarity with model evaluation, validation, and quality assurance processes • Understanding of monitoring and observability principles as applied to ML systems • Strong problem-solving skills and a bias toward automation over manual processes

🏖️ Benefícios

• Medical, dental, vision benefits • Annual wellness stipend • Mental health support • Life, STD, LTD Income Insurance Plans • Unlimited PTO • Generous paid parental leave • Flexible schedule • 12 Paid US company holidays • Quarterly personal productivity stipend • One-time stipend for home office upgrades • 401(k) plan with company match • Tax Savings Programs • Learning / Education stipend • Participation in talks and conferences • Employee Resource Groups • AI enablement workshops / sessions