Quantiphi
Technical Architect - ML
USA - Remote · Senior
Sponsorship not specifiedDetected 31 days ago
PythonSQLSnowflakeDatabricksVector DatabasesAWSKubernetesTerraformHelmCI/CDPrometheusGrafanaDevOpsMachine LearningAirflowData EngineeringLLMsRAGLLMOpsMLOpsA/B TestingCybersecurityExcelCommunication
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odds of building a lasting career here
40Risky
Cap-exempt (no lottery)0
Sponsors this role90
Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70
Thin sponsorship signal and lottery-bound. A low-probability bet with your clock running. Prioritize cap-exempt roles and proven entry-level sponsors first.
Lottery odds assume a STEM candidate.
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About the role
- While technology is the heart of our business, a global and diverse culture is the heart of our success.
- We love our people and we take pride in catering them to a culture built on transparency, diversity, integrity, learning and growth.
- If working in an environment that encourages you to innovate and excel, not just in professional but personal life, interests you- you would enjoy your career with Quantiphi!
Responsibilities
- 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 (EKS-first) while evaluating alternative orchestration tools with similar capabilities (Kubeflow, SageMaker, MLflow, Airflow, etc.).
- Define and implement standards for model deployment, monitoring, governance, and automation to ensure production-grade reliability and scalability.
- Collaborate with cross-functional teams - data engineering, platform, DevOps, and client stakeholders - to deliver production-ready ML solutions.
Requirements
- 8+ years working in ML/AI engineering or MLOps roles with strong architecture exposure.
- Hands-on experience with at least one major
- Strong experience with AWS SageMaker (Pipelines, Feature Store, Model Registry, Model Monitor).
- Experience implementing ML CI/CD pipelines including automated training, testing, validation, model promotion, and endpoint deployment.
- Experience working on Infrastructure as Code (IaC) tools and CI/CD pipelines
- Experience with Kubernetes based development
- Experience with feature engineering pipelines and Feature Store management.
- Hands-on experience with AWS Bedrock and Agentcore service
- Experience with CloudWatch, SageMaker Model Monitor, Prometheus/Grafana.
- Strong expertise in AWS cloud-native ML stack, including: SageMaker(primary), EKS, Lambda, API Gateway, CI/CD (CodeBuild/CodePipeline or equivalent)
Nice to have
- Strong communication and cross-team collaboration skills.
Skills
- Experience with vector databases, RAG pipelines, or multi-agent AI systems.
- 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.
- Familiarity with Observability stacks (Prometheus, Grafana, CloudWatch, OpenTelemetry).
- SQL and data transformation experience using Snowflake, Databricks, Spark.
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This listing is sourced directly from Quantiphi's careers page and normalized into a canonical job model.