Job Title: AI/MLOps Engineer
Location: 100% Remote (EST)
Duration: 6-Month Contract-to-Hire
Overview
We are seeking an AI/MLOps Engineer to design, build, and operationalize machine learning solutions within an Azure-based ecosystem. This role focuses on productionizing data science models, building scalable ML pipelines, and enabling advanced analytics through robust data and AI platforms.
You will partner closely with data scientists, data engineers, and DevOps teams to move models from experimentation into reliable, secure, and scalable production environments.
Key Responsibilities
- Design, develop, and deploy AI/ML solutions using Azure services (e.g., Azure Machine Learning, Cognitive Services)
- Build and maintain end-to-end machine learning pipelines for training, testing, deployment, and monitoring
- Productionize models developed by data scientists, ensuring scalability, performance, and reliability
- Implement CI/CD pipelines to support continuous integration and delivery of data, code, and models
- Automate model deployment, monitoring, and lifecycle management processes
- Monitor model performance and retrain/update models as needed
- Integrate AI capabilities into enterprise data workflows and applications
- Develop solutions leveraging NLP, computer vision, recommendation systems, and chatbot technologies
- Collaborate cross-functionally with Data Engineering, Data Science, and DevOps teams
- Ensure security, governance, and compliance of ML systems and data pipelines
Required Qualifications
- Bachelor's degree in Computer Science, Information Systems, or related field (or equivalent experience)
- 3 6 years of experience in software/data engineering or machine learning, including recent hands-on MLOps experience
- Strong programming skills in Python (preferred), Java, or Scala
- Experience with ML frameworks such as TensorFlow, PyTorch, or scikit-learn
- Hands-on experience with Azure cloud services, especially Azure Machine Learning
- Experience with containerization and orchestration tools (Docker, Kubernetes)
- Familiarity with MLOps tools such as MLflow, Kubeflow, or similar
- Experience building and deploying ML models into production environments
Preferred Experience
- Experience implementing NLP, computer vision, or recommendation systems
- Exposure to chatbot or conversational AI frameworks
- Experience working in regulated environments (e.g., healthcare, finance)
- Familiarity with enterprise data platforms and modern data architectures
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