Developer- MLOPS

Required Qualifications:

· 3+ 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 shraddha@careerstone.in

Note:- Candidates with maximum 1 month Notice period are preferred.