This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for an AWS MLOps Engineer based in the United States.
This role sits at the intersection of machine learning, cloud engineering, and production-grade software development, focused on building and scaling end-to-end ML systems in AWS environments.
You will be responsible for operationalizing machine learning models—from training and experimentation through deployment and monitoring in production.
The position involves working with modern MLOps frameworks, CI/CD pipelines, and distributed cloud services to ensure scalable, reliable, and secure ML workflows.
You will collaborate closely with data scientists, backend engineers, and platform teams to bring models into real-world production use.
This is a highly technical, hands-on engineering role where automation, performance, and system reliability are key priorities.
You will contribute to building intelligent, data-driven applications that directly impact customer experiences at scale.
The environment is fast-paced, cloud-native, and centered on continuous innovation in AI/ML systems.
Accountabilities:
- Design, build, and maintain end-to-end ML pipelines covering training, evaluation, deployment, versioning, and monitoring of models in production.
- Develop and optimize MLOps workflows using tools such as MLflow, Spark ML, and Python-based ML frameworks.
- Implement CI/CD pipelines for machine learning systems using GitHub Actions and other automation tools.
- Deploy and manage scalable ML services on AWS using ECS, ECR, API Gateway, S3, RDS, and Application Load Balancer.
- Build and maintain backend services and APIs using FastAPI and REST-based architectures.
- Work with SQL and PostgreSQL databases, including schema design and ORM-based data modeling (SQLAlchemy).
- Monitor model performance in production and implement alerting, logging, and optimization strategies.
- Collaborate with cross-functional teams to ensure reliability, scalability, and security of ML systems.
Requirements:
- 2+ years of experience in MLOps or Machine Learning Engineering roles focused on production ML systems.
- Strong hands-on experience with MLflow, Spark ML, Python, and common ML libraries.
- Proven experience in model lifecycle management including training, versioning, deployment, and monitoring.
- Experience building CI/CD pipelines and using GitHub Actions for automated deployments.
- Solid AWS experience with services such as ECS, ECR, API Gateway, S3, RDS, and Application Load Balancer.
- 1+ year of backend development experience using FastAPI and REST APIs.
- Strong knowledge of SQL, relational databases, PostgreSQL, and ORM frameworks such as SQLAlchemy.
- Familiarity with production-grade system design for scalable ML applications.
- Exposure to Databricks (Unity Catalog, Jobs, Workflows) and/or Agentic AI is a plus.
- Strong problem-solving, communication, and collaboration skills in distributed engineering environments.
- Bachelor’s degree in Computer Science, Engineering, Data Science, or related field (or equivalent experience).
Benefits:
- Competitive base salary ranging from $92,250 to $120,000, plus performance-based incentives.
- Comprehensive medical, dental, and vision insurance coverage.
- 401(k) retirement savings plan with employer participation.
- Paid time off, holidays, and dedicated paid learning days.
- Flexible remote work structure within the United States.
- Employee assistance programs supporting well-being and work-life balance.
- Career development opportunities in advanced AI and cloud-native engineering environments.
- Exposure to large-scale, production AI/ML systems and modern cloud technologies.