Senior MLOps / LLM Engineer

Description We’re seeking an experienced DevOps / LLMOps or MLOps / LLMOps Engineer to design, deploy, and maintain the infrastructure supporting Large Language Model (LLM) platforms. This role blends DevOps best practices with AI / ML expertise, with a focus on building and scaling agentic AI systems that integrate directly with LLMs. You’ll drive development of intelligent, autonomous agents while ensuring reliability, scalability, and performance across training, fine-tuning, and deployment pipelines.

English fluency is a MUST for this role! Only candidates with level C1 or C2 will be considered :

A1 Beginner A2 Elementary B1 Intermediate B2 Upper-Intermediate C1 Advanced C2 Proficient

Responsibilities Own strategy and execution of agentic AI systems using LLMs and autonomous decision-making components Build prototypes and scale them into MVPs demonstrating real-world AI viability Collaborate with engineering and product teams to embed LLM agents into user-facing features Design and manage CI / CD pipelines for LLM training and deployment workflows Provide technical leadership on DevOps, security, and deployment strategies for AI / ML models

Requirements 5+ years building and deploying ML or GenAI systems in production, with strong architectural and systems-level ownership 2+ years of proven experience with LLM integration, LLM-based agents, or autonomous reasoning systems End-to-end AI / ML delivery : data prep, training, evaluation, deployment, monitoring Cloud expertise (AWS, GCP, or Azure) with CI / CD pipelines, multi-tenant deployments, and infrastructure automation Experience with Databricks and familiarity with Langchain / GenAI toolkits Strong agility and ability to deliver in fast-moving product environments

Nice To Have Advanced degree (MS / Ph.D.) in ML, Computer Science, or related field Deep expertise in DevOps / MLOps practices and scaling secure, enterprise-grade AI workflows Broader data engineering knowledge, especially in Databricks Background in aviation industry