Job description
Project Role : LLM Operations Engineer
Project Role Description : Utilize cloud-native services and tools for scalable and efficient deployment. Monitor LLM performance, address operational challenges, and ensure compliance and security standards in AI operations.
Must have skills : Machine Learning Operations
Good to have skills : NA
Minimum 3 year(s) of experience is required
Educational Qualification : 15 years full time education
Summary:
As a LLM Operations Engineer, you will utilize cloud-native services and tools for scalable and efficient deployment. Your typical day will involve monitoring the performance of large language models, addressing operational challenges, and ensuring that all activities comply with security standards in AI operations. You will collaborate with various teams to enhance operational efficiency and contribute to the overall success of AI initiatives within the organization.
Roles & Responsibilities:
- Expected to be an SME.
- Collaborate and manage the team to perform.
- Responsible for team decisions.
- Engage with multiple teams and contribute on key decisions.
- Provide solutions to problems for their immediate team and across multiple teams.
- Facilitate knowledge sharing sessions to enhance team capabilities.
- Develop and implement best practices for operational efficiency.
Professional & Technical Skills:
- Must To Have Skills: Proficiency in Machine Learning Operations.
- Good To Have Skills: Experience with cloud-native platforms such as AWS, Azure, or Google Cloud.
- Strong understanding of model deployment strategies and monitoring tools.
- Experience with containerization technologies like Docker and Kubernetes.
- Familiarity with compliance and security standards in AI operations.
Additional Information:
- The candidate should have minimum 5 years of experience in Machine Learning Operations.
- This position is based at our Bengaluru office.
- A 15 years full time education is required.
This job post has been translated by AI and may contain minor differences or errors.
