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athenahealth - Lead MLOps Engineer - Python / Cloud Computing

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

(ref : hirist.tech)

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