ML Ops Engineer

About the Role

The role involves contributing to the GenAI technical stack by participating in architecture, development, integration, testing, documentation, and maintenance of new features. Engineers must ensure that development follows DevSecOps recommendations and best practices, and they will produce example LLM/LLMOps/Agentic/RAG applications to illustrate platform capabilities. You will design CI/CD workflows that automate integration and deployment of GenAI artifacts across environments and help define the LLMOps operating model that the platform enforces. Collaboration with internal stakeholders to co‑design features and occasional embedding within stakeholder teams to provide expertise and support are also required. Ongoing duties include conducting technical watch on emerging GenAI topics, benchmarking new products and technologies, and contributing insights to the platform’s evolution. These activities help prepare the organization for future AI innovations. • Develop and integrate new GenAI features into the AI platform, adhering to DevSecOps best practices. • Create example LLM/LLMOps/Agentic/RAG applications to showcase platform capabilities. • Build CI/CD pipelines to automate deployment of GenAI artifacts across environments. • Define and enforce the LLMOps operating model for the AI platform. • Collaborate with internal stakeholders to co‑design features and provide expert support. • Conduct technical watch, benchmark emerging GenAI technologies, and assess new products. • Deploy and operate production‑grade Generative AI applications (Agentic, RAG, ARAG). • Apply agile methodologies (Scrum, SAFe) with a rigorous yet adaptable mindset.

Key Responsibilities

• ▸genai development
• ▸ci/cd pipelines
• ▸llmops model
• ▸demo applications
• ▸technical watch
• ▸production deployment

What You Bring

Successful candidates will have proven experience as an LLMOps or AI engineer, including deployment of production‑grade Generative AI applications such as Agentic, RAG, or ARAG. They must possess strong knowledge of LLMs, master DevOps tools (CI/CD, GitHub, GitHub Actions), be proficient in Python, and have deep expertise in Azure AI services or AWS AI services; familiarity with LangChain products and Databricks is a plus. Candidates should be comfortable with agile methodologies (Scrum, SAFe), demonstrate a rigorous yet adaptable mindset, work autonomously while being a good team player, and enjoy coaching and supporting others. Effective communication in English and the ability to thrive in an international work environment are essential. • Master DevOps tools (CI/CD, GitHub, GitHub Actions) and Python programming. • Demonstrate expertise in Azure AI services or AWS AI services; familiarity with LangChain and Databricks is a plus. • Communicate effectively in English, coach teammates, and thrive in a global environment.

Requirements

• ▸python
• ▸ci/cd
• ▸azure ai
• ▸aws ai
• ▸langchain
• ▸llmops

Work Environment

Hybrid