What are the responsibilities and job description for the MLOps Engineer position at Talent Groups?
Job Title: MLOps Engineer
Location: Minneapolis, MN (Hybrid – 3 Days Onsite)
Employment Type: 6 Months Contract
Job Summary
We are seeking a skilled MLOps Engineer to join our team in Minneapolis, MN. The ideal candidate will have hands-on experience building, deploying, and maintaining scalable machine learning pipelines and production-ready ML systems.
You will work closely with data scientists, software engineers, and cloud teams to operationalize machine learning models while ensuring reliability, scalability, and performance in a Google Cloud Platform (Google Cloud Platform) environment.
Key Responsibilities
- Design, develop, and maintain scalable machine learning pipelines and workflows.
- Deploy, monitor, and maintain machine learning models in production environments.
- Collaborate with data scientists, software engineers, and business stakeholders to operationalize ML solutions.
- Build and optimize data engineering pipelines using Python.
- Implement best practices for model versioning, monitoring, governance, and CI/CD.
- Maintain and deploy model endpoints using the most efficient serving frameworks.
- Optimize cloud infrastructure and ML workflows for performance, scalability, and cost efficiency.
- Troubleshoot production issues and continuously improve ML platform reliability.
- Support end-to-end machine learning lifecycle from training through deployment and monitoring.
Required Qualifications
- 3–5 years of experience in MLOps, Machine Learning Engineering, or a related field.
- Strong proficiency in Python, with experience building data engineering pipelines.
- Hands-on experience with Google Cloud Platform (Google Cloud Platform).
- Experience working with Vertex AI and Cloud Build.
- Strong knowledge of BigQuery SQL.
- Experience with Docker and containerization technologies.
- Solid understanding of the machine learning lifecycle, including training, deployment, monitoring, and model serving.
- Experience deploying and maintaining model endpoints using optimal serving frameworks.
- Strong problem-solving and troubleshooting skills.
- Excellent communication and collaboration skills.
Preferred Qualifications
- Experience with FastAPI or similar API frameworks.
- Knowledge of batch and real-time model deployment strategies.
- Experience with Kubernetes and release management processes.
- Familiarity with CI/CD pipelines for ML applications.
- Experience monitoring production ML systems and implementing observability best practices.
Experience
- 3–5 years of relevant experience in MLOps, Machine Learning Engineering, or a related discipline.
Technical Skills
Python , Google Cloud Platform (Google Cloud Platform) , Vertex AI , Cloud Build , BigQuery SQL , Docker , Kubernetes (Preferred) , FastAPI (Preferred) , Machine Learning Model Deployment , MLOps , CI/CD , Model Monitoring , Data Engineering