Technical Architect – ML

🕒 Agosto 4

🇺🇸 Estados Unidos – Remoto (EUA)

⏰ Tempo Integral

🟠 Sênior

🔴 Especialista

🔙 Engenheiro Backend

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Quantiphi

SiteLinkedInTodas as Vagas

1001 - 5000 funcionários

Fundada em 2013

💼 Consultoria

🏥 Saúde

📦 Logística

💰 Series A em 2019-12

Consulting • Healthcare • Logistics

A Quantiphi é uma empresa líder em engenharia digital orientada por IA que utiliza uma década de experiência no setor para capacitar negócios por meio de soluções de IA escaláveis, seguras e adaptáveis. Ao integrar tecnologia de ponta com aplicações do mundo real, a Quantiphi transforma organizações em vários setores, incluindo saúde, finanças, educação e varejo. Seus serviços abrangem aplicações de IA, análises de dados, modernização de infraestrutura em nuvem e implementações personalizadas de IA. A Quantiphi faz parcerias com gigantes da tecnologia como AWS, Google Cloud, NVIDIA e outros para impulsionar a adoção de IA e oferecer oportunidades transformacionais para empresas.

Descrição

• Architect and implement the MLOps strategy for the programme, ensuring alignment with the project proposal and delivery roadmap • Design and own enterprise-grade ML/LLM pipelines covering model training, validation, deployment, versioning, monitoring, and CI/CD automation • Build container-oriented ML platforms with an EKS-first approach while evaluating alternative orchestration tools • Implement hybrid MLOps and LLMOps workflows, including prompt/version governance, evaluation frameworks, and monitoring for LLM-based systems • Serve as a technical authority across multiple internal and customer projects, contributing architectural patterns, best practices, and reusable frameworks • Enable observability, monitoring, drift detection, lineage tracking, and auditability across ML/LLM systems • Define and implement standards for model deployment, monitoring, governance, and automation to ensure production-grade reliability and scalability • Collaborate with data engineering, platform, DevOps, and client stakeholders to deliver production-ready ML solutions • Ensure solutions adhere to security, governance, and compliance expectations around cloud services, Kubernetes workloads, and MLOps tools • Conduct architecture reviews, troubleshoot complex ML system issues, and guide teams through implementation across cloud-native ML platforms • Mentor engineers and provide guidance on modern MLOps tools, platform capabilities, and best practices

🎯 Requisitos

• 8+ years working in ML/AI engineering or MLOps roles with strong architecture exposure • Strong expertise in AWS cloud-native ML stack, including SageMaker, EKS, Lambda, API Gateway, and CI/CD (CodeBuild/CodePipeline or equivalent) • Hands-on experience with at least one major MLOps toolset and awareness of alternatives: MLflow, Kubeflow, SageMaker Pipelines, Airflow, BentoML, KServe, Seldon • Deep understanding of model lifecycle management • Experience implementing or supporting LLMOps pipelines, including prompt versioning, evaluation metrics, and automation frameworks • Deep understanding of ML lifecycle: data ingestion, feature engineering, training, evaluation, model packaging, CI/CD, drift detection, monitoring, and governance • Strong experience with AWS SageMaker, including Pipelines, Feature Store, Model Registry, and Model Monitor • Experience implementing ML CI/CD pipelines including automated training, testing, validation, model promotion, and endpoint deployment • Experience working on Infrastructure as Code tools and CI/CD pipelines • Experience with Kubernetes-based development • Experience with feature engineering pipelines and Feature Store management • Understanding of lineage tracking, including training data snapshots, feature versions, code versioning, metadata tracking, and reproducibility • Hands-on experience with AWS Bedrock and Agentcore service • Experience with CloudWatch, SageMaker Model Monitor, Prometheus/Grafana • Strong foundation in Python and cloud-native development patterns • Solid understanding of security best practices, IAM, secrets management, and artifact governance • Experience with vector databases, RAG pipelines, or multi-agent AI systems • Exposure to DevOps and infrastructure-as-code, including Terraform, Helm, and CDK • Hands-on understanding of model drift detection, A/B testing, canary rollouts, and blue-green deployments • Familiarity with observability stacks, including Prometheus, Grafana, CloudWatch, and OpenTelemetry • SQL and data transformation experience using Snowflake, Databricks, and Spark • Ability to translate business goals into scalable AI/ML platform designs • Strong communication and cross-team collaboration skills • Ability to guide engineering teams through technical uncertainty and design choices

🏖️ Benefícios

• Global and diverse culture • Culture built on transparency, diversity, integrity, learning and growth • Environment encouraging innovation and professional and personal excellence