AI Model Operations Engineer
4 days ago
At Dimiour, we are seeking an exceptional AI Model Operations Engineer to join our team. This role will play a critical part in managing and enhancing our internal Large Language Model (LLM) observability platform.
Key Responsibilities- Design and implement end-to-end MLOps/LLMOps pipelines to integrate seamlessly with the observability platform.
- Develop and optimize APIs using FastAPI to facilitate smooth interactions between data science and the observability platform.
- Collaborate with cross-functional teams to integrate observability features into the LLM lifecycle, enhancing model monitoring and debugging capabilities.
- Establish and maintain robust monitoring, logging, and alerting systems for LLM applications.
In this role, you will work closely with data scientists and engineers to troubleshoot issues, optimize performance, and enhance overall system reliability. You will also provide guidance and support to engineering teams on best practices for LLM deployment and maintenance.
We estimate the salary for this position to be around $120,000 - $180,000 per year, depending on experience and location.
Qualifications- Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
- 3+ years of experience in machine learning, deep learning, and natural language processing.
- Knowledge of NLP and experience with specific LLM frameworks (e.g., Hugging Face Transformers).
- Proficiency in MLOps/LLMOps tools and technologies, including Kubernetes, Docker, and orchestration frameworks.
- Strong API development skills, particularly with FastAPI, and a solid understanding of RESTful principles.
- In-depth knowledge of the ML/LLM lifecycle, with a focus on observability and monitoring aspects.
- Expertise in version control systems (e.g., Git) and collaborative development workflows.
- Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and experience deploying ML/LLM models in cloud environments.
Please note that experience in designing, developing, and training Large Language Models is preferred but not required. Familiarity with automation and configuration management tools, as well as container orchestration tools like Kubernetes, is also a plus.
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