Key Responsibilities: CI/CD Pipelines: Design, implement, and manage CI/CD pipelines for AI/ML products, facilitating seamless integration and delivery across development, testing, and production environments.
Infrastructure as Code (IaC): Develop and maintain IaC using tools like Terraform, Ansible, or AWS CloudFormation to ensure scalable and consistent infrastructure management.
Cloud Management: Manage cloud services (AWS, GCP, Azure) to deploy and maintain AI-based solutions, optimizing resources and cost efficiency.
Model Deployment & Monitoring: Automate model deployment processes and set up monitoring for AI models in production to track performance, drift, and other key metrics.
Containerization: Use Docker and orchestration tools like Kubernetes to create, deploy, and manage containers for various AI/ML workloads.
Security & Compliance: Implement security best practices, including managing access controls, data encryption, and vulnerability scanning.
Collaboration: Work closely with data scientists, ML engineers, and other cross-functional teams to translate requirements into scalable and reliable AI solutions.
Troubleshooting & Optimization: Monitor system performance, identify issues, and optimize AI application infrastructure for speed, efficiency, and reliability.
Qualifications: Education: Bachelor's degree in Computer Science, Engineering, or a related field.
Relevant certifications in DevOps, AI/ML, or Cloud Services are a plus.
Experience: 3-5 years of experience in DevOps or similar roles, with experience in AI/ML product deployment.
Technical Skills: Proficiency in CI/CD tools (Jenkins, GitLab CI, CircleCI) Experience with cloud platforms (AWS, Azure, GCP) Strong knowledge of containerization (Docker, Kubernetes) Familiarity with IaC (Terraform, Ansible, CloudFormation) Proficiency in scripting languages (Python, Bash) AI/ML Knowledge: Understanding of AI/ML model lifecycle management, including deployment, monitoring, and retraining workflows.
Problem-Solving: Ability to identify and resolve issues related to scalability, latency, and reliability in AI systems.
Soft Skills: Strong communication, collaboration, and documentation skills.
Nice-to-Have: Experience with MLOps frameworks (Kubeflow, MLflow) Familiarity with data processing tools (Apache Spark, Kafka) Exposure to serverless architecture and microservices Understanding of model governance, bias detection, and AI ethics