athenahealth - Lead MLOps Engineer - Python/Cloud Computing
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athenahealth - Lead MLOps Engineer - Python / Cloud Computing
athenaHealth Technology Private Limited.Bangalore
30+ days ago
Job description
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 Responsibilities Execution (50%)
Produce clear and detailed technical design specifications for ML and cloud-based solutions.
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 to the team (30%) :
Take ownership of deployed models and ensure their continuous improvement.
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 Coordination and Communication (10%) :
Collaborate with technology and product teams to align ML initiatives with business goals.
Share technical knowledge and insights across teams to enhance collective and Leadership (10%) :
Mentor and support junior engineers to improve overall team productivity.
Promote best practices and innovation within the team to drive MLOps success