Machine Learning Deployment Specialist
6 days ago
LION AND ELEPHANTS is seeking a highly skilled Machine Learning Deployment Specialist to join our team. As a key member of our AI engineering team, you will be responsible for designing, building, and managing our machine learning deployment pipelines.
Key Responsibilities:
- Design and implement deployment strategies for various tools and ML models, considering scalability, cost, and ease of use.
- Communicate and educate the team on design decisions, alternatives considered, and the future impact of these strategies on ML deployments.
- Develop deployment strategies for technologies including Docker containers, PyTorch models, XGBoost / scikit-learn models.
- Ensure the security of proprietary work and manage access policies to safeguard intellectual property while enabling internal team collaboration.
- Automate testing, enforce code quality, and implement development best practices to prevent future deployment issues and enhance the team's ability to create solutions rapidly.
- Collaborate with the team to improve infrastructure and identify knowledge gaps while meeting project deadlines.
- Empower team members by fostering understanding and application of development practices that support the ML deployment pipeline.
- Be open to learning and teaching, adapting to evolving tools and models efficiently.
Basic Qualifications:
- Proven experience in MLOps or a similar role, particularly in deploying machine learning models to production.
- Experience in designing, building, and managing MLOps pipelines.
- Experience with cloud computing, especially AWS.
- Experience in building and managing API endpoints.
- Proficient with MLOps tools such as MLFlow, KubeFlow, or AirFlow.
- Familiarity with ML tools and frameworks like pandas, numpy, scikit-learn, and PyTorch.
- Experience with Infrastructure as Code tools such as CloudFormation or Terraform.
- Strong problem-solving skills and the ability to work independently or collaboratively.
Nice to Have:
- Strong experience with container management and deployment using Kubernetes, AWS, ECR, AWS Fargate, or AWS Batch.
- Prior experience building an end-to-end MLOps pipeline for deep learning models from scratch.
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