MLOps Architect
1 week ago
Title: MLOps Architect
As an MLOPS Architect, you will be a part of an Agile team to build healthcare applications and implement new features while adhering to the best coding development standards.
Responsibilities: -
• The Senior MLOps Architect will be responsible for architecting and implementing scalable MLOps solutions.
• This role involves working closely with data scientists, data engineers, and software developers to ensure that machine learning models are efficiently deployed, monitored, and maintained.
• The ideal candidate will have a strong background in both machine learning and software engineering, with experience in deploying ML models in production environments.
• Architecture & Design: Design and implement scalable MLOps architectures and frameworks that support model development, deployment, monitoring, and lifecycle management.
• Pipeline Development: Develop, maintain, and optimize ML pipelines for data preprocessing, model training, validation, and deployment.
• Automation: Implement automation for CI/CD pipelines, testing, and monitoring of ML models in production. Infrastructure Management: Design and manage cloud-based and on-premises infrastructure for ML model training and deployment, including Kubernetes, Docker, and other relevant technologies.
• Collaboration: Work closely with data scientists, data engineers, software engineers, and DevOps teams to integrate ML models into existing systems and workflows.
• Monitoring & Performance: Implement monitoring and logging solutions to track model performance, data drift, and other critical metrics.
• Security & Compliance: Ensure that ML models and pipelines adhere to security best practices and regulatory requirements.
• Documentation & Best Practices: Create and maintain comprehensive documentation of MLOps processes, tools, and best practices.
• Mentorship & Leadership: Provide technical leadership and mentorship to junior team members and help build a culture of continuous learning and improvement.
Educational Qualifications: -
• Engineering Degree – BE/ME/BTech/MTech/BSc/MSc.
• Technical certification in multiple technologies is desirable.
Mandatory skills
• Technical Skills: Strong programming skills in Python, Java, or another programming language.
• Experience with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-Learn).
• Proficiency in cloud platforms (e.g., AWS, Azure, Google Cloud) and related services (e.g., S3, EC2, Lambda, Glue, SageMaker etc.).
• Experience with containerization and orchestration technologies (e.g., Docker, Kubernetes).
• Knowledge of CI/CD tools (e.g., Jenkins, GitLab CI/CD, CircleCI).
• Familiarity with infrastructure-as-code tools (e.g., Terraform, CloudFormation).
• Strong understanding of data engineering principles and ETL processes. Soft Skills: Excellent problem-solving and analytical skills. Strong communication and interpersonal skills.
• Ability to work in a fast-paced, collaborative environment.
• Strong organizational and time management skills.
• Preferred Qualifications: Experience with model versioning and monitoring tools (e.g., MLflow, Kubeflow). Knowledge of data privacy and security regulations.
- • Previous experience in the healthcare industry or payer domain.Initially 3-4 weeks onsite after that remote
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