Technical Architect - ML

Quantiphi; United States; Remote; USD 150,000 - 210,000; Full time

Job summary

Quantiphi is seeking a Technical Architect - ML with 8+ years in ML/AI engineering or MLOps to architect and implement enterprise-grade ML/LLM pipelines in a remote USA setting. You will lead design, governance, and automation across model lifecycles, leveraging SageMaker, Kubeflow, Airflow, and Bedrock.

Strong IAM/security focus and cross-functional collaboration are required. The role emphasizes cloud-native development, observability, and scalable deployment with CI/CD, Terraform/CDK, and

Qualifications

• 8+ years in ML/AI engineering or MLOps with architecture exposure.
• Strong expertise in AWS cloud-native ML stack, including SageMaker, EKS, Lambda, API Gateway, CI/CD.
• Hands-on with major MLOps toolsets: MLflow, Kubeflow, SageMaker Pipelines, Airflow, BentoML, KServe, Seldon.
• Deep understanding of model lifecycle management from feature engineering to deployment and monitoring.
• Experience with LLMops pipelines, prompt versioning and evaluation metrics.
• Strong ML lifecycle knowledge: data ingestion, feature engineering, training, evaluation, packaging, CI/CD, drift detection, monitoring, governance.
• AWS Bedrock and Agentcore exposure; CloudWatch, SageMaker Model Monitor, Prometheus/Grafana.
• Python proficiency and cloud-native patterns; security, IAM, secrets management, and artifact governance.

Responsibilities

• Architect and implement the MLOps strategy for the programme, aligned with project roadmap.
• Design and own enterprise-grade ML/LLM pipelines covering training, validation, deployment, versioning, monitoring and CI/CD.
• Build container-oriented ML platforms (EKS-first) while evaluating Kubeflow, SageMaker, MLflow, Airflow, etc.
• Implement hybrid MLOps + LLMOps workflows including prompt governance and evaluation frameworks.
• Serve as technical authority across internal and customer projects, sharing patterns and reusable frameworks.
• Enable observability, drift detection, lineage tracking and auditability across ML/LLM systems.
• Define standards for model deployment, monitoring, governance, and automation for reliability and scalability.
• Collaborate with data engineering, platform, DevOps and client stakeholders to deliver production ML solutions.
• Ensure security, governance and compliance for cloud services, Kubernetes workloads and MLOps tools.
• Mentor engineers on modern MLOps tools, platforms and best practices.

Skills

ML/AI engineering

MLOps

LLMOps

Python

Cloud-native dev

IAM security

Cross-team collaboration

Tools

SageMaker

Kubeflow

MLflow

Airflow

Seldon

BentoML

KServe

Terraform

Helm

CDK

Agentcore

Bedrock

CloudWatch

Prometheus

Grafana

Job description

## Technical Architect - MLApplylocations: USA - Remotetime type: Full timeposted on: Posted Yesterdaytime left to apply: End Date: September 5, 2026 (30 days left to apply)job requisition id: JR11587While 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!****Must have skills & Qualifications:***** **8+ years working in ML/AI engineering or MLOps roles with strong architecture exposure.*** **Strong expertise in **AWS cloud-native ML stack**, including: SageMaker(primary), EKS, Lambda, API Gateway, 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** (feature engineering->training → registry → deployment → monitoring).*** **Experience implementing or supporting **LLMOps pipelines**, including: prompt versioning, evaluation metrics, 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** (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**.*** **Understanding of **lineage tracking**: training data snapshot, feature versions, code versioning, metadata tracking, 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.******Good to have 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.*** **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.******Key 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.).*** **Implement hybrid **MLOps + 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 cross-functional teams — data engineering, platform, DevOps, and client stakeholders — to deliver production-ready ML solutions.*** **Ensure all solutions adhere to **security, governance, and compliance expectations**, particularly around handling 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.***If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us**!*