Pay
$166K–$240K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 1,279 postings in the last year. Stated ranges, not salaries paid.
Technical Architect - ML
USA - Remote
Open 3 months, first seen by us 91 days ago- Pay
- Pay not listedSimilar roles state $166K–$240K
- Work setting
Remote — work setting source
Listed location: USA - Remote
Read the full posting- Employment
Full-time — employment source
Employment type in the employer’s posting: Full time
From the employer’s posting
What you’ll work on
Full postingArchitect the MLOps strategy for the programme and its delivery roadmap
Design and own ML/LLM pipelines from training to CI/CD automation
Build EKS-first container ML platforms
Conduct architecture reviews and troubleshoot complex ML system issues
From the employer’s posting
Architect and implement the MLOps strategy for the programme
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)
Conduct architecture reviews, troubleshoot complex ML system issues, and guide teams through implementation across cloud-native ML platforms
Posting record
Report an errorOpen 3 monthsWe first saw this posting 91 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
- Status in our records
- Live
- First seen by us
- Aug 5, 202691 days ago
- Recorded sightings
- 126
- Last seen by us
- Nov 4, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Time since we first saw it
Day 91
Up longer than most. Similar jobs usually come down between day 6 and day 69.
From 4,301 similar postings that came down. Coming down is not always a hire.
Quantiphi is hiring
30 open jobs
Open jobs by the week we first saw them, last 12 weeks.
- ML Engineer hiring has slowed
- Takes jobs down at a usual pace
- 3% of open jobs were reposted
See how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Python
- SQL
- AWS
- Databricks
- Grafana
- Kubernetes
- MLflow
- SageMaker
- Snowflake
- Spark
- Terraform
- Airflow
Where the posting names them
- 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.
- 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.
- 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 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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).
- 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.
- 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.
Your next step
Complete your application on quantiphi.wd1.myworkdayjobs.com. The employer’s form will show what is required.
Applying through Workday
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Already applied? Track this application
About the role
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
US government figures for computer and information research scientists
- Median yearly pay, May 2025
- $140,300
- Expected change, 2025 to 2035
- 21.8% more jobsagainst 3.5% for all jobs
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
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!
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
USA - Remote
- 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.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.