Technical Architect – ML

🔥 0 minutes ago

🇺🇸 United States – Remote

⏰ Full Time

🟠 Senior

🔴 Lead

🔙 Backend Engineer

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Quantiphi

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 2013

💼 Consulting

🏥 Healthcare

📦 Logistics

💰 Series A on 2019-12

Consulting • Healthcare • Logistics

Quantiphi is a leading AI-first digital engineering company that leverages a decade of industry expertise to empower businesses through scalable, secure, and adaptable AI solutions. By integrating cutting-edge technology with real-world applications, Quantiphi transforms organizations across various sectors including healthcare, finance, education, and retail. Their services span AI applications, data analytics, cloud infrastructure modernization, and custom AI implementations. Quantiphi partners with technology giants like AWS, Google Cloud, NVIDIA, and others to drive AI adoption and deliver transformational opportunities for enterprises.

📋 Description

• Architect and implement the MLOps strategy for the programme, aligning it 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, prioritizing EKS, 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 internal and customer projects by 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 • Collaborate with data engineering, platform, DevOps, and client stakeholders to deliver production-ready ML solutions • Ensure solutions adhere to security, governance, and compliance expectations • Conduct architecture reviews, troubleshoot complex ML system issues, and guide implementation across cloud-native ML platforms • Mentor engineers on modern MLOps tools, platform capabilities, and best practices

🎯 Requirements

• 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 tools such as CodeBuild or CodePipeline • Hands-on experience with at least one major MLOps toolset and awareness of alternatives including MLflow, Kubeflow, SageMaker Pipelines, Airflow, BentoML, KServe, and Seldon • Deep understanding of model lifecycle management, including feature engineering, training, registry, deployment, and monitoring • Experience implementing or supporting LLMOps pipelines, including prompt versioning, evaluation metrics, and automation frameworks • Understanding of the complete ML lifecycle: data ingestion, feature engineering, training, evaluation, model packaging, CI/CD, drift detection, monitoring, and governance • Strong experience with AWS SageMaker Pipelines, Feature Store, Model Registry, and Model Monitor • Experience implementing ML CI/CD pipelines with automated training, testing, validation, model promotion, and endpoint deployment • Experience with 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, and Grafana • Strong foundation in Python and cloud-native development patterns • Solid understanding of security best practices, IAM, secrets management, and artifact governance • Good to have: vector databases, RAG pipelines, or multi-agent AI systems • Good to have: DevOps and infrastructure-as-code tools such as Terraform, Helm, and CDK • Good to have: model drift detection, A/B testing, canary rollouts, and blue-green deployments • Good to have: observability stacks including Prometheus, Grafana, CloudWatch, and OpenTelemetry • Good to have: SQL and data transformation 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

🏖️ Benefits

• Culture built on transparency, diversity, integrity, learning and growth • Ample opportunities to learn, grow and interact with colleagues from varied experience and backgrounds around the globe • Hybrid work culture (company-wide description)