AI Resume Builder
JOIN NOW Post Job
Primary Talent Partners
AI

MLOps Engineer

Primary Talent Partners · Charlotte, NC · $158k - $178k

Actively hiring Posted 4 months ago

Responsibilities

  • Design, develop, and deploy machine learning models using AWS SageMaker platform.
  • Build and maintain ML pipelines for training, validation, and deployment of models.
  • Implement MLOps best practices including CI/CD for machine learning workflows.
  • Collaborate with data scientists to productionize research models.
  • Monitor model performance and implement automated retraining processes.
  • Optimize model inference performance and cost efficiency.
  • Develop and maintain model versioning and experiment tracking systems.
  • Ensure data quality and implement data validation frameworks.
  • Create comprehensive documentation and technical specifications.
  • Participate in code reviews and maintain high coding standards.
  • Debug Terraform and Concourse errors.
  • Proactively update pipelines based on changes made by other organizations.
  • Migrate repository to GitHub and update pipelines accordingly.

Basic qualifications

  • Bachelor's degree in Computer Science, Data Science, Engineering, or related field; or 8 years of equivalent work experience.
  • 3+ years of experience in machine learning engineering or related roles.
  • Proficiency in Python programming with experience in ML libraries (pandas, numpy, etc.).
  • Familiarity with Infrastructure as Code (Terraform, CloudFormation).
  • Hands-on experience with AWS SageMaker for model training, tuning, and deployment.
  • Strong background in data science methodologies and statistical analysis.
  • Deep understanding of MLOps practices and tools (Docker, Kubernetes, CI/CD pipelines).
  • Experience with version control systems (Git Hub Actions) and collaborative development.
  • Knowledge of cloud platforms, preferably AWS (S3, EC2, Lambda, etc.).

Preferred qualifications

  • Master's degree in a relevant field.
  • AWS certifications (Machine Learning Specialty, Solutions Architect, etc.).
  • Knowledge of containerization and orchestration technologies.
  • Experience with monitoring and observability tools (CloudWatch, Prometheus, etc.).
  • Experience with big data technologies (EMR, Spark, Hadoop, etc.).
  • Understanding of software engineering best practices and design patterns.
  • Good working experience in ETL (SSIS or Sqoop/Spark).
  • Experience with EMR
  • Expert SQL knowledge (All types of Joins, CTE’s, Indexes, Stored Procedures, SQL performance).
  • Knowledge in building basic machine learning models (Classification & Regression).
  • Knowledge in Docker/MLOps and its orchestrations.
  • Strong analytical and problem-solving abilities.
  • Excellent communication and collaboration skills.
  • Ability to work in fast-paced, agile environments.
  • Detail-oriented with a focus on code quality and documentation.
  • Continuous learning mindset and adaptability to new technologies.
  • Experience working cross-functionally with data scientists, engineers, and product teams.

Tags & focus areas

Used for matching and alerts on DevFound
Contract Machine Learning Data Science Mlops Generative Ai Ai
Common Questions

Frequently asked questions

Quick answers about how DevFound's AI matching, resumes, and referrals work.

DevFound's AI Copilot ingests your profile, goals, and live job data to deliver curated matches in seconds. Every match includes a resume variant, suggested referrals, and interview prep so you can act immediately. The more feedback you provide, the sharper the Copilot becomes.

AI-led job searches shrink the hours spent sifting through boards and formatting resumes. DevFound pairs automation with your personal outreach, so you reserve energy for interviews and negotiation. Traditional networking still matters, but AI gives you a lift before you even send a message.

Modern AI roles expect comfort with production-grade code, data fluency, and practical ML tooling. The strongest candidates pair deep technical chops with storytelling—translating model impact to product, GTM, and exec partners. Continuous learning keeps you ahead as stacks evolve.

DevFound rewards active seekers. Keep your profile fresh, respond to match quality prompts, and enable alerts so you never miss a role. The AI prioritizes companies and teams that align with your feedback, accelerating both introductions and interview invites.

High-density tech hubs continue to host the deepest AI talent pools, yet distributed teams are catching up fast. Use DevFound filters to hone in on onsite, hybrid, or fully remote roles and watch openings expand across time zones.

DevFound aggregates thousands of remote AI openings and flags the nuances—core hours, async culture, and visa needs—up front. The Copilot also recommends how to position your distributed work experience so hiring managers know you can thrive on a remote team.

Still have questions?

Contact Support