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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer - **Company:** MARKET STREET TALENT INC - **Location:** Portland, ME, United States - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Amazon S3, Cloud Computing, Cloud Engineering, Continuous Integration, Information Engineering, Data Governance, Database Applications, Github, Monitoring of Systems, Identity and Access Management, Python (Programming Language), Linux System Administration, Machine Learning, Standard Sql, Azure Machine Learning, Apache Spark, Pyspark, Gitlab-ci, Deployment Automation, Performance Monitor, Machine Learning Operations, Terraform, Docker, Databricks - **Published:** August 2, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=4be4c11ded3f3dae ## About the Role * 3-5 years of experience in MLOps, Machine Learning Engineering, Data Engineering, or related fields * Strong Python programming skills with experience developing production-grade, maintainable code * Hands-on experience with AWS services, including: * S3 * SageMaker * Lambda * ECS/EKS * IAM * Experience working with Databricks, including: * Jobs and workflows * MLflow * Unity Catalog * Experience implementing and supporting CI/CD pipelines using tools such as GitHub Actions, GitLab CI, or similar platforms * Experience with Terraform or other Infrastructure-as-Code technologies * Familiarity with Docker and containerization concepts * Understanding of machine learning lifecycle management, including: * Experiment tracking * Model registries * Model monitoring * Retraining strategies * Strong Linux administration and troubleshooting skills * Experience working with SQL and data-driven applications * Excellent problem-solving and analytical abilities * Strong communication and collaboration skills * Ability to work effectively within cross-functional teams Nice-to-Haves * Experience with Spark or PySpark in large-scale processing environments * Knowledge of data governance, security, and compliance best practices * AWS Certifications * Databricks Certifications * Experience supporting enterprise MLOps platforms and AI initiatives * Familiarity with model observability and performance monitoring solutions ## Description We are looking for a talented Machine Learning Engineer to join the team of our exceptional client. This role is focused on building, deploying, and maintaining the infrastructure and automation that powers machine learning solutions in production environments., This position requires a hands-on MLOps professional with experience supporting the full machine learning lifecycle, from model training and deployment through monitoring and optimization. The ideal candidate will have a strong foundation in cloud technologies, automation, and modern ML platforms, along with a passion for building reliable, scalable, and efficient machine learning systems., * Design, build, and maintain scalable machine learning infrastructure and production pipelines * Develop and support end-to-end ML workflows across training, deployment, monitoring, and retraining processes * Build and maintain machine learning solutions using AWS and Databricks platforms * Implement CI/CD processes that automate model deployment and lifecycle management * Create reproducible and reliable machine learning workflows that support enterprise-scale applications * Optimize compute resources, workloads, and infrastructure costs while maintaining system performance * Support model versioning, experiment tracking, and governance requirements * Collaborate closely with data scientists and engineering teams to operationalize machine learning models * Monitor production model performance and help ensure reliability, scalability, and accuracy over time * Develop infrastructure-as-code solutions to support deployment and operational consistency * Support containerized application deployment strategies and cloud-native ML environments * Troubleshoot and resolve issues across infrastructure, pipelines, and production machine learning systems ## Related Videos - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Innovating Developer Tools with AI: Insights from GitHub Next](https://www.wearedevelopers.com/videos/1268-innovating-developer-tools-with-ai-insights-from-github-next) - [Effective Machine Learning - Managing Complexity with MLOps](https://www.wearedevelopers.com/videos/185-effective-machine-learning-managing-complexity-with-mlops) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Empowering Retail Through Applied Machine Learning](https://www.wearedevelopers.com/videos/976-empowering-retail-through-applied-machine-learning) ## Related Articles - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path)