LLMOps Engineer

Thales people architect identity management and data protection solutions at the heart of digital security. Business and governments rely on us to bring trust to the billons of digital interactions they have with people. Our technologies and services help banks exchange funds, people cross borders, energy become smarter and much more. More than 30,000 organizations already rely on us to verify the identities of people and things, grant access to digital services, analyze vast quantities of information and encrypt data to make the connected world more secure.

JOB OBJECTIVE:

• As a member of the AI/GenAI Squad you will actively contribute to the development and deployment of AI and generative AI solutions for Thales employees.
• As a Data Scientist, you will need a deep understanding of hardware requirements, Kubernetes environments, and network configurations to create a robust and scalable infrastructure for LLM deployment.

ROLES & RESPONSIBILITIES:

Architecture Design and Implementation:

• Define and implement a scalable and efficient LLMOps architecture tailored to the specific needs of large language model applications.
• Select and recommend the appropriate hardware configurations that are compliant and optimized for LLM workload, focusing on GPU utilization and efficiency.
• Integrate and configure the architecture to operate seamlessly within a Kubernetes environment, ensuring high availability and scalability.

Kubernetes Configuration:

• Design and implement Kubernetes solutions to manage and serve GPU resources effectively, ensuring that these resources are accessible and optimized for LLM projects.
• Develop and maintain Kubernetes clusters, pods, and containers to support the deployment and operation of large language models.

Network and Storage Solutions:

• Design and configure network solutions that ensure secure, reliable, and efficient data flow within the LLMOps architecture.
• Implement storage solutions that are scalable, durable, and optimized for the high data throughput requirements of LLM applications.

Automation and Orchestration:

• Utilize Ansible for automation and orchestration to streamline the deployment and management of the LLMOps infrastructure.
• Ensure that the infrastructure can be easily monitored, scaled, and updated through automated processes.

QUALIFICATION, CERTIFICATION & EDUCATIONAL REQUIREMENTS:

LLM Infrastructure Expertise:

• Proven experience in building and maintaining production-grade LLM infrastructure, demonstrating a deep understanding of the challenges and best practices associated with large language models.

2. Kubernetes Proficiency:

• Extensive experience in building Kubernetes solutions, specifically tailored to serve GPU resources for LLM projects. Familiarity with Kubernetes orchestration, networking, and storage management is essential.

3. Hardware Knowledge:

• Strong understanding of hardware requirements for LLMs, particularly in selecting and configuring GPU resources to maximize performance and efficiency.

4. Networking and Storage Skills:

• Expertise in network architecture and configuration, with the ability to design solutions that ensure security, reliability, and efficiency.
• Proficiency in implementing scalable and high-performance storage solutions suitable for LLM workloads.

5. Automation and Orchestration:

• Experience with Ansible or similar tools for automation and orchestration, enabling efficient deployment and management of LLM infrastructure.

PREFERRED SKILLS:

• Fluency in English, both written and verbal.
• Excellent communication and presentation skills to technical and non-technical people
• Team work and willing to perform trough cooperation.
• Strong attention to details.
• Willingness for change and solving problem.
• Be passionate about digital transformation.
• Strong analytical mindset.
• Data oriented.

#LI-MM1

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