MLOPs Developer

Role Overview: As an MLOps Developer, your primary responsibility will be to design, develop, and maintain complex, high-performance, and scalable MLOps systems that interact with AI models and systems. You will work closely with cross-functional teams, including data scientists, AI researchers, and AI/ML engineers, to understand requirements, define project scope, and ensure alignment with business goals. Additionally, you will provide technical leadership in choosing, evaluating, and implementing software technologies in a cloud-native environment. Troubleshooting intricate software problems and contributing to the development of CI/CD pipelines and high-performance data pipelines will also be part of your role. Key Responsibilities: - Design, develop, and maintain complex, high-performance, and scalable MLOps systems interacting with AI models and systems. - Cooperate with cross-functional teams to understand requirements, define project scope, and ensure alignment with business goals. - Provide technical leadership in choosing, evaluating, and implementing software technologies in a cloud-native environment. - Troubleshoot and resolve intricate software problems ensuring optimal performance and reliability with AI/ML systems. - Participate in software development project planning and estimation for efficient resource allocation and timely solution delivery. - Contribute to the development of CI/CD pipelines and high-performance data pipelines. - Drive integration of GenAI models in production workflows and support edge deployment use cases via model optimization and containerization for edge runtimes. - Contribute to the creation and maintenance of technical documentation including design specifications, API documentation, data models, and user manuals. Qualifications Required: - 6+ years of experience in machine learning operations or software/platform development. - Strong experience with Azure ML, Azure DevOps, Blob Storage, and containerized model deployments on Azure. - Strong knowledge of programming languages commonly used in AI/ML such as Python, R, or C++. - Experience with Azure cloud platform, machine learning services, and best practices. Preferred Qualifications: - Experience with machine learning frameworks like TensorFlow, PyTorch, or Keras. - Experience with version control systems such as Git and CI/CD tools like Jenkins, GitLab CI/CD, or Azure DevOps. - Knowledge of containerization technologies like Docker and Kubernetes, and infrastructure-as-code tools such as Terraform or Azure Resource Manager (ARM) templates. - Hands-on experience with monitoring LLM outputs, feedback loops, or LLMOps best practices. - Familiarity with edge inference hardware like NVIDIA Jetson, Intel Movidius, or ARM CortexA/NPU devices.,