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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