Senior AI Engineer – Data, MLOps

🕒 Agosto 25

🍂 Massachusetts – Remoto

🍂 Massachusetts – Remote

💵 $133.900 - $223.900 / ano

⏰ Tempo Integral

🟠 Sênior

🚰 Engenheiro de Dados

👻 Score fantasma 8%

O que significa: quão provável esta vaga está aberta sem contratação ativa.Como ler: 60% ou mais significa que achamos que é uma vaga fantasma. Pontuações mais baixas preocupam menos.Como pontuamos: tempo em aberto, republicações, vagas abertas vs tamanho do time, rapidez para fechar vagas, e se o salário está divulgado.Observação: é uma estimativa de dados públicos, não um julgamento da empresa. Vagas reais podem ter pontuação alta, então use como uma dica, não como um fato.

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Teradyne

SiteLinkedInTodas as Vagas

5001 - 10000 funcionários

Fundada em 1960

🏭 Manufatura

🔧 Hardware

🤝 B2B

Manufacturing • Hardware • B2B

A Teradyne é um fornecedor de soluções para testes e automação na fabricação. Suas empresas oferecem automação industrial e equipamentos de teste automatizados em diversos setores e aplicações, ajudando os clientes a alcançar maiores volumes de produção, melhoria na qualidade e maior retorno sobre o investimento. A Teradyne expandiu-se por meio da aquisição de especialistas como LitePoint (teste wireless), Universal Robots (robôs colaborativos) e MiR (robôs móveis), consolidando um portfólio que abrange testes de semicondutores e eletrônicos, robótica industrial e automação fabril.

Descrição

• Design, build, and operationalize machine learning and AI solutions for Teradyne’s IT organization • Translate business problems into predictive and classification models, GenAI assistants, RAG workflows, and intelligent process automation • Design, develop, train, evaluate, and deploy end-to-end ML and generative AI solutions • Build and integrate AI agents with enterprise data sources, APIs, and MCP servers • Apply experimentation, model selection, and evaluation to deliver accurate and performant solutions • Own the transition of internally developed AI solutions from build to production • Define and certify release readiness according to AI Platform Operations Team standards • Implement model lifecycle management, including versioning, model registry, retraining, promotion, and deprecation • Define drift, degradation, and retraining criteria and drive tuning and remediation • Provide technical input for runbooks, on-call processes, and handoff standards • Engineer reusable, multi-environment MLOps/LLMOps pipelines • Build CI/CD, testing, and infrastructure/configuration-as-code for models, prompts, and agents • Establish automated offline and online evaluation and regression testing • Define technical standards, reference patterns, reusable frameworks, processes, and playbooks • Mentor and upskill engineers on ML/AI, MLOps/LLMOps, and agentic AI • Implement responsible AI practices, security controls, observability, logging, and audit trails • Monitor solution performance, quality, reliability, and cost and drive continuous improvement • Report to the Enterprise AI/Data Product Manager within Enterprise Architecture and Data

🎯 Requisitos

• 8–10 years of experience in ML/AI or software engineering • At least 3 years building and operating production ML/AI systems • Recent hands-on experience with generative and agentic AI • Bachelor’s or advanced degree in Computer Science, Data Science, Engineering, or a related field • Strong proficiency in Python and SQL • Experience with scikit-learn, PyTorch, or TensorFlow • Hands-on experience with Azure AI Foundry, Microsoft Copilot Studio, Anthropic Claude, Cursor, Google Vertex AI, or Snowflake Cortex AI • Proven MLOps/LLMOps experience, including multi-environment pipelines, CI/CD, Azure DevOps or GitHub Actions, Azure Machine Learning, MLflow, and model lifecycle management • Experience with agentic AI orchestration frameworks, RAG, vector databases and embeddings, prompt engineering, MCP servers, and APIs • Experience transitioning AI solutions from development into production ownership, including monitoring, drift detection, retraining, and support • Strong knowledge of model evaluation and testing, AI observability, responsible AI, AI security, and governance frameworks • Multicloud experience with Microsoft Azure as primary and Google Cloud as secondary • Experience integrating with enterprise data on Snowflake and Microsoft Fabric • Proven ability to mentor and upskill teams • Strong collaboration and communication skills across technical and business teams • Analytical mindset focused on measurable business outcomes

🏖️ Benefícios

• Discretionary bonus(es) based on financial performance • Medical insurance • Dental insurance • Vision insurance • Flexible Spending Accounts • Retirement savings plans • Life insurance • Disability insurance • Paid vacation and holidays • Tuition assistance programs