Machine Learning Engineer
Pay Rate: $75$89/hour
Position Summary
We are seeking a skilled Machine Learning Engineer (MLOps) to support the full lifecycle of machine learning models, including design, development, deployment, and maintenance. This role focuses on building scalable, production-ready AI/ML solutions and ensuring seamless integration within existing systems.
The ideal candidate will collaborate with cross-functional teams to deploy, monitor, and optimize machine learning models that drive operational efficiency, innovation, and data-driven decision-making. This position requires strong experience in MLOps, DevOps practices, and cloud-based AI infrastructure.
Key Responsibilities- Design, build, deploy, and maintain machine learning models in production environments
- Develop and manage end-to-end MLOps pipelines, including model versioning, monitoring, and automation
- Implement scalable ML infrastructure using cloud platforms (AWS, Azure, or GCP)
- Build and optimize CI/CD pipelines for automated testing and deployment of ML models
- Collaborate with data scientists, data engineers, and DevOps teams to operationalize AI solutions
- Monitor model performance, system health, and data drift; implement logging and alerting solutions
- Ensure reliability, scalability, and performance of ML systems in real-time inference environments
- Maintain version control for models and code to support reproducibility and collaboration
- Apply best practices for testing, debugging, and performance optimization
- Ensure compliance with data security, privacy, and regulatory standards
- Create and maintain technical documentation for ML systems and processes
Required Qualifications- Bachelors degree in Computer Science, Engineering, Artificial Intelligence, or a related field
- 3+ years of experience in machine learning engineering or MLOps
- Hands-on experience managing the end-to-end machine learning lifecycle
- Strong programming skills in Python, R, and/or SQL
- Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform
- Experience with containerization (Docker) and orchestration tools (Kubernetes)
- Experience with infrastructure as code tools such as Terraform
- Experience building and maintaining CI/CD pipelines (e.g., GitHub Actions)
- Strong understanding of software development, system architecture, and deployment processes
- Experience with monitoring, logging, and performance tuning of ML systems
- Knowledge of version control systems (e.g., Git)
Preferred Qualifications- Masters degree in Computer Science, Engineering, or a related field
- Experience working with healthcare data or regulated environments
- Familiarity with Electronic Health Record (EHR) systems
- Experience with predictive modeling, natural language processing (NLP), and large language models (LLMs)
- Knowledge of retrieval-augmented generation (RAG) frameworks and their applications
- Understanding of agile methodologies and DevOps lifecycle practices
Core Competencies- Production-grade ML model deployment and lifecycle management
- Scalable infrastructure design for AI/ML workloads
- Cross-functional collaboration and technical leadership
- Strong analytical and problem-solving skills
- Effective technical communication and documentation