- 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.
