Job Title: Full Stack DevOps Engineer Location: India (Remote)

Job Type: Full-Time

Position Overview:

We are seeking a Full Stack DevOps Engineer specializing in Cloud Solutions and Machine Learning to join our team. This role requires expertise in both software development and DevOps, with a focus on managing and optimizing cloud-based solutions for medical imaging while also integrating machine learning (ML) models for automated diagnostics and image analysis. You will work on CI/CD automation, cloud infrastructure management, cloud-based solutions optimization, and ML model deployment to enhance medical imaging workflows.

Key Responsibilities:
Development & Cloud Solution Management:

• Develop, optimize, and maintain Cloud infrastructure for efficient storage, retrieval, and transmission of medical images.
• Implement DICOM (Digital Imaging and Communications in Medicine), HL7 (Health Level 7), and FHIR standards for seamless integration with RIS (Radiology Information Systems) and HIS (Hospital Information Systems).
• Develop custom APIs and middleware to improve interoperability.
• Optimize database performance for high-speed medical image retrieval.

Machine Learning Model Deployment in Cloud Solutions:

• Deploy, integrate, and manage machine learning models within the workflow to automate medical image analysis.
• Implement AI-driven image classification, anomaly detection, and diagnostic assistance models for radiologists.
• Work with data scientists and ML engineers to ensure smooth deployment and scalability of AI models in real-world clinical environments.
• Optimize model inference speed, accuracy, and resource utilization on cloud or edge computing platforms.
• Ensure regulatory compliance (HIPAA, GDPR, FDA) for AI-driven healthcare applications.

DevOps & Infrastructure Automation:

• Design, implement, and manage CI/CD pipelines for Cloud Solutions and AI model deployment.
• Maintain and optimize cloud-based infrastructure using AWS, GCP, or Azure.

• Implement containerization (Docker) and orchestration (Kubernetes) for scalable and secure deployments.
• Automate cloud infrastructure provisioning and configuration using Terraform.
• Monitor system performance, storage utilization, and ML model efficiency to ensure optimal uptime.
• Troubleshoot cloud solution failures, AI model performance issues, and system bottlenecks.

Security & Compliance:

• Ensure compliance with HIPAA, GDPR, and FDA regulations for medical imaging and AI deployment.
• Implement end-to-end encryption, access controls, and authentication mechanisms for patient data security.
• Conduct regular security audits, vulnerability assessments, and penetration testing. Collaboration & Documentation:

• Work closely with software developers, data scientists, IT administrators, and healthcare professionals to enhance AI-driven workflows.
• Provide technical support and troubleshooting for Cloud Solutions and AI-related issues in clinical settings.
• Document cloud solution configurations, AI model deployment workflows, and best practices for team knowledge sharing.

Required Skills & Experience:

• 3+ years of experience in DevOps and cloud-based solutions.
• Strong expertise in cloud platforms (AWS, GCP, Azure) and managing cloud-based environments.
• Experience with CI/CD tools (GitHub Actions, GitLab CI, Jenkins) for automation and AI model deployment.
• Hands-on experience with containerization (Docker) and orchestration (Kubernetes).
• Experience with Infrastructure as Code (Terraform).
• Proficiency in monitoring and logging tools (Grafana).
• Strong knowledge of DICOM, HL7, and FHIR for healthcare data exchange.
• Experience with deploying machine learning models in cloud or edge computing environments.
• Proficiency in Python, TensorFlow, PyTorch for AI model integration.
• Strong knowledge of networking, VPNs, firewalls, and security best practices.
• Familiarity with database management (PostgreSQL, MySQL) for medical image storage.

Preferred Skills:

• Experience with serverless computing (AWS Lambda, Azure Functions).

• Experience in cloud-based medical imaging solutions (PACS, RIS, HIS, DICOM, HL7)
• Knowledge of medical data archiving and long-term storage solutions.
• Experience with edge computing for AI model inference in clinical settings.
• Understanding of data replication and disaster recovery strategies.