Hiring: Technical Architect - ML Company: Quantiphi Location: United States Job Posted Time: 2026-09-17 10:36:40 Employment Type: Hybrid Target Skills & Keywords : A/B Testing, API Gateway, AWS, Airflow, CDK, CI/CD, CloudWatch, Databricks, EKS, Feature Store, Grafana, Helm, IAM, IaC, Infrastructure as Code, Kubeflow, Kubernetes, LLM, Lambda, MLOps, MLflow, Microsoft Excel, Model Registry, OpenTelemetry, Prometheus, Python, RAG, SQL, SageMaker, Snowflake, Spark, Terraform About the job Experience: •8+ years working in ML/AI engineering or MLOps roles with strong architecture exposure. Required Skills: •Exposure to DevOps and infrastructure-as-code (Terraform, Helm, CDK). •Hands-on understanding of model drift detection, A/B testing, canary rollouts, and blue-green deployments. •Operational familiarity with Observability stacks (Prometheus, Grafana, CloudWatch, OpenTelemetry). •SQL and data transformation experience using Snowflake, Databricks, Spark. •Demonstrated capacity to translate business goals into scalable AI/ML platform designs. •Strong communication and cross-team collaboration skills. •Demonstrated capacity to guide engineering teams through technical uncertainty and design choices. •Architect and implement the MLOps strategy for the programme, ensuring alignment with the project proposal and delivery roadmap. Qualifications: •Strong expertise in AWS cloud-native ML stack, including: SageMaker(primary), EKS, Lambda, API Gateway, CI/CD (CodeBuild/CodePipeline or equivalent) •Applied hands-on capability in at least one major MLOps toolset and awareness of alternatives: MLflow, Kubeflow, SageMaker Pipelines, Airflow, BentoML, KServe, Seldon. •Comprehensive expertise in model lifecycle management (feature engineering->training → registry → deployment → monitoring). •Comprehensive expertise in 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, Model Monitor). •Understanding of lineage tracking: training data snapshot, feature versions, code versioning, metadata tracking, reproducibility. •Applied hands-on capability in AWS Bedrock and Agentcore service •Strong foundation in Python and cloud-native development patterns. Interested candidates, please apply directly through the job posting on company's career page or try via AI auto apply on this platform. Don't miss this opportunity to join a forward-thinking team!