MLOPs Developer (NV610FCT RM 3522)

Position: MLOPs Developer (NV610FCT RM 3522)

Job Description:
Required Qualifications:

• 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.

Roles:

• Design, develop, and maintain complex, high-performance, and scalable MLOps systems that
  interact with AI models and systems.
• Cooperate 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.
• Offer technical leadership and expertise in choosing, evaluating, and implementing software
  technologies, tools, and frameworks in a cloud-native (Azure + AML) environment.
• Troubleshoot and resolve intricate software problems, ensuring optimal performance and
  reliability when interfacing with AI/ML systems.
• Participate in software development project planning and estimation, ensuring efficient
  resource allocation and timely solution delivery.
• Contribute to the development of continuous integration and continuous deployment
  (CI/CD) pipelines.
• Contribute to the development of high-performance data pipelines, storage systems, and
  data processing solutions.
• Drive integration of GenAI models (e.g., LLMs, foundation models) in production workflows,
  including prompt orchestration and evaluation pipelines.
• Support edge deployment use cases via model optimization, conversion (e.g., to ONNX,
  TFLite), and containerization for edge runtimes.
• Contribute to the creation and maintenance of technical documentation, including design
  specifications, API documentation, data models, data flow diagrams, and user manuals.

Preferred Qualifications:

• Experience with machine learning frameworks such as TensorFlow, PyTorch, or Keras.
• Experience with version control systems, such as Git, and CI/CD tools, such as Jenkins, GitLab
  CI/CD, or Azure DevOps.
• Knowledge of containerization technologies like Docker and Kubernetes, and infrastructureas-code tools such as Terraform or Azure Resource Manager (ARM) templates.
• Experience with Generative AI workflows, including prompt engineering, LLM fine-tuning, or
  retrieval-augmented generation (RAG).
• Exposure to GenAI frameworks: LangChain, LlamaIndex, Hugging Face Transformers, OpenAI
  API integration.
• Experience deploying optimized models on edge devices using ONNX Runtime, TensorRT,
  OpenVINO, or TFLite.
• Hands-on 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.

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