athenahealth - Lead MLOps Engineer - Python/Cloud Computing

Ideal Qualifications :

- Bachelors degree in Computer Science, Software Engineering, or a related discipline.

- 7 to 12 years of experience in software engineering, with expertise in MLOps, cloud computing, and scalable architectures.

- Strong object-oriented programming skills, preferably in Python.

- Hands-on experience in developing and deploying microservices in any public cloud environment such as AWS, Azure, or GCP.

- Expertise in Kubernetes, including designing, deploying, and maintaining enterprise-class ML models and services.

- Experience in Kubeflow, maintaining and optimizing ML pipelines for efficient model training and deployment.

- Proven experience in deploying and maintaining Linux-based, highly scalable, and fault-tolerant enterprise platforms.

- Hands-on experience with Terraform or CloudFormation for infrastructure automation and cloud resource management.

- Familiarity with monitoring and logging tools such as Grafana, Prometheus, and CloudWatch.

- Strong understanding of cloud security, service mesh architectures (Istio), and scalable ML deployment best practices.

- Experience working with databases such as Snowflake, PostgreSQL, MySQL, Redis, and DynamoDB.

- Proficiency in configuration management and CI/CD tools like Jenkins, Puppet, Chef, and Bottlerocket.

Job Responsibilities :

Technical Execution (50%)

- Develop, test, and deploy high-quality software components that align with security, performance, and scalability requirements.

- Design and maintain Kubernetes-based ML model deployments in cloud environments.

- Optimize and manage Kubeflow pipelines for model training, deployment, and monitoring.

- Implement cloud infrastructure automation using Terraform or CloudFormation.

- Ensure best practices in cloud security, monitoring, and scalability.

- Conduct unit testing, functional testing, and peer code reviews to maintain code quality and reliability.

Contribution to the team (30%) :

- Participate actively in agile ceremonies such as stand-ups, sprint planning, retrospectives, and backlog grooming.

- Work collaboratively with data scientists, ML engineers, and software developers to integrate ML models into production environments.

Cross-functional Coordination and Communication (10%) :

- Share technical knowledge and insights across teams to enhance collective expertise.

Mentorship and Leadership (10%) :

- Promote best practices and innovation within the team to drive MLOps success