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Architecture Jobs in Portland, OR

MLOps Architect

Info Way Solutions

Portland, OR

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JOB DETAILS
SKILLS
AWS Lambda, Amazon Elastic Compute Cloud (EC2), Amazon Simple Storage Service (S3), Amazon Web Services (AWS), Analysis Skills, Automation, Best Practices, Cloud Computing, Communication Skills, Computer Programming, Continuous Deployment/Delivery, Continuous Integration, Data Modeling, Data Science, Database Extract Transform and Load (ETL), DevOps, Docker, Documentation, Healthcare, Information/Data Security (InfoSec), Interpersonal Skills, Java, Jenkins, Leadership, Machine Learning, Mentoring, Metrics, Microsoft Windows Azure, Model Validation, Organizational Skills, Performance Analysis, Performance Modeling, Privacy Controls, Problem Solving Skills, Production Systems, Programming Languages, Python Programming/Scripting Language, Regulations, Regulatory Requirements, Software Development, Software Engineering, Team Player, Technical Leadership, Testing, Time Management
LOCATION
Portland, OR
POSTED
Today
Role: MLOps Architect
Location: Portland, OR (Remote) - (initial 3-4 weeks Onsite)
Type: Fulltime

Job Description:

Responsibilities: -
  • The Senior MLOps Architect will be responsible for architecting and implementing scalable MLOps solutions.
  • This role involves working closely with data scientists, data engineers, and software developers to ensure that machine learning models are efficiently deployed, monitored, and maintained.
  • The ideal candidate will have a strong background in both machine learning and software engineering, with experience in deploying ML models in production environments.
  • Architecture & Design: Design and implement scalable MLOps architectures and frameworks that support model development, deployment, monitoring, and lifecycle management.
  • Pipeline Development: Develop, maintain, and optimize ML pipelines for data preprocessing, model training, validation, and deployment.
  • Automation: Implement automation for CI/CD pipelines, testing, and monitoring of ML models in production. Infrastructure Management: Design and manage cloud-based and on-premises infrastructure for ML model training and deployment, including Kubernetes, Docker, and other relevant technologies.
  • Collaboration: Work closely with data scientists, data engineers, software engineers, and DevOps teams to integrate ML models into existing systems and workflows.
  • Monitoring & Performance: Implement monitoring and logging solutions to track model performance, data drift, and other critical metrics.
  • Security & Compliance: Ensure that ML models and pipelines adhere to security best practices and regulatory requirements.
  • Documentation & Best Practices: Create and maintain comprehensive documentation of MLOps processes, tools, and best practices.
  • Mentorship & Leadership: Provide technical leadership and mentorship to junior team members and help build a culture of continuous learning and improvement.
Mandatory skills:
  • Technical Skills: Strong programming skills in Python, Java, or another programming language.
  • Experience with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-Learn).
  • Proficiency in cloud platforms (e.g., AWS, Azure, Google Cloud) and related services (e.g., S3, EC2, Lambda, Glue, SageMaker etc.).
  • Experience with containerization and orchestration technologies (e.g., Docker, Kubernetes).
  • Knowledge of CI/CD tools (e.g., Jenkins, GitLab CI/CD, CircleCI).
  • Familiarity with infrastructure-as-code tools (e.g., Terraform, CloudFormation).
  • Strong understanding of data engineering principles and ETL processes. Soft Skills: Excellent problem-solving and analytical skills. Strong communication and interpersonal skills.
  • Ability to work in a fast-paced, collaborative environment.
  • Strong organizational and time management skills.
  • Preferred Qualifications: Experience with model versioning and monitoring tools (e.g., MLflow, Kubeflow). Knowledge of data privacy and security regulations.
  • Previous experience in the healthcare industry or payer domain.Initially 3-4 weeks onsite after that remote.

About the Company

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Info Way Solutions

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