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 infrastructures-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.Interested Candidates can also share the resumes on
Note : - Candidates with maximum 1 month Notice period are preferred.