Technical Architect - ML

Quantiphi; United States; Remote; USD 180,000 - 260,000; Full time

Job summary

Quantiphi is seeking an experienced ML/AI engineering leader to architect and deliver enterprise-grade MLOps and LLMOps pipelines on AWS. You will shape model lifecycle, governance, and end-to-end deployment strategies across cloud-native platforms.

You will collaborate with data engineering, platform, and DevOps teams to build scalable, secure ML platforms (EKS-first) and ensure production-grade reliability with observability and compliance.

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, 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: feature engineering → training → registry → deployment → monitoring.
• Experience implementing or supporting LLMOps pipelines, including prompt versioning and evaluation metrics.
• Deep understanding of ML lifecycle: data ingestion, feature engineering, training, evaluation, packaging, CI/CD, drift monitoring.
• Strong experience with AWS SageMaker (Pipelines, Feature Store, Model Registry, Model Monitor).
• Experience with ML CI/CD pipelines including automated training, testing, validation, promotion, and endpoint deployment.
• Experience with Infrastructure as Code (IaC) tools and CI/CD pipelines.
• Experience with Kubernetes-based development and feature store management.
• Understanding of lineage tracking: data snapshots, feature versions, code/versioning, reproducibility.
• Hands-on with AWS Bedrock and Agentcore services, CloudWatch, SageMaker Monitor, Prometheus/Grafana.
• Strong Python skills and cloud-native development patterns.
• Solid understanding of security, IAM, secrets management, and artifact governance.

Responsibilities

• Architect and implement the MLOps strategy aligned with proposal and delivery roadmap.
• Design and own enterprise-grade ML/LLM pipelines for 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 a technical authority across internal and customer projects, contributing patterns and reusable frameworks.
• Enable observability, monitoring, drift detection, lineage tracking, and auditability across ML/LLM systems.
• Define standards for deployment, monitoring, governance, and automation to ensure production-grade reliability.
• Collaborate with data engineering, platform, DevOps, and client stakeholders to deliver production-ready ML solutions.
• Ensure security, governance, and compliance, especially around cloud services and Kubernetes workloads.
• Conduct architecture reviews, troubleshoot complex ML system issues, and guide teams across platforms.
• Mentor engineers and guide on modern MLOps tools and practices.

Skills

ML/AI engineering

MLOps

Python

Cloud-native development

Security best practices

Tools

SageMaker

EKS

Kubeflow

Airflow

Seldon

Terraform

CI/CD (CodeBuild/CodePipeline)

Kubernetes

Feature Store

Model Registry

Job description

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!

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_!_