Lead MLOps Engineer (Multimodal Agentic AI Systems)

Job description

About CONXAI CONXAI has built a no-code, agentic AI platform for the Architecture, Engineering and Construction (AEC) and physical industries, focused on knowledge-automation . We automate high-stakes, knowledge-intensive workflows traditionally trapped in siloed data, fragmented tools and tacit (undocumented) human expertise. Our multi-agent systems perform complex reasoning in the physical world; and transform bespoke, service-heavy processes into scalable Service-as-a-Software automation. CONXAI is trusted by some of the leading AEC companies in Europe, US, LATAM and Japan. Your Role Automate the lifecycle management of Agentic AI and Large Vision Model As the Lead MLOps Engineer, you are the bridge between experimental ML models and scalable, reliable enterprise software. You will be responsible for the "factory line" of our AI - from training automation to the deployment of agentic tools. You’ll ensure our multi-agent systems (LLMs + Computer Vision) remain performant, cost-effective, and accurate. What You’ll Do Agentic Orchestration: Build and optimize the infrastructure for LangChain/LangGraph , enabling complex multi-agent reasoning Training Automation: Develop automated pipelines for fine-tuning LLMs and training Computer Vision models specifically for industry use cases Model Deployment: Containerize and deploy models using Docker and Terraform , ensuring low-latency inference for high-stakes workflows Lifecycle Management: Implement monitoring for AI "silent failures," tracking model drift and performance metrics to ensure consistent customer success ML Infrastructure: Manage the compute-heavy environments required for AI, optimizing for both performance and unit economics Who You Are 5+ years in MLOps or ML Engineering, with experience in both NLP (LLMs) and Computer Vision Agentic Expert: Deep familiarity with agentic frameworks like LangChain or LangGraph Tech Stack: Expert in Terraform, Docker, and GitLab CI/CD pipelines Strategic Mindset: You understand that an AI model is only as good as its production reliability and its impact on the user’s ROI Why CONXAI Edge of Innovation: Architect the production backbone for real-time, low-latency agentic AI High Autonomy: Drive the end-to-end MLOps strategy, from automated retraining pipelines to sophisticated model monitoring at scale Top-Tier Peer Group: Partner with a global team of ML researchers and software engineers to bridge the gap between "experimental" and "mission-critical" Equity & Scale: Competitive compensation with significant equity upside