Technical Architect – ML

🕒 August 4

🇺🇸 United States – Remote

⏰ Full Time

🟠 Senior

🔴 Lead

🔙 Backend Engineer

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👻 Ghost score 48%

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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, 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 using 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 for 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

🎯 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 (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, including feature engineering, training, registry, deployment, and monitoring • 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 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/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, or 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, or 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 • Environment encouraging innovation and professional and personal growth

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