MLOps Engineer

Applico Capital is the leading venture capital firm focused on the $8 trillion B2B distribution industry. Through our learnings and understanding of the industry, we are building a tech startup, currently in stealth, to solve the industry's biggest problems as it comes to unlocking AI-enabled synergies.

Our mandate is to leverage AI and modern technologies to reimagine the role of the traditional distributor and transform how the entire industry operates.

We are looking for highly technical builders who thrive in entrepreneurial, scrappy, and collaborative environments.

We are seeking an MLOps Engineer to design and implement the infrastructure, automation, and monitoring that enable machine learning to be reliable, repeatable, and scalable. You will enable our AI Scientists and Engineers to move faster, while ensuring compliance, observability, and cost efficiency.

This is a scrappy, hands-on role in a startup-style team where building durable, automated systems is as important as moving quickly. You’ll ensure that ML becomes a dependable part of daily business operations. You will also extend MLOps practices to support agentic AI systems, managing orchestration, monitoring emergent behavior, and ensuring the safe and governed use of AI-augmented workflows.

• Build CI/CD pipelines for ML models across training, deployment, and monitoring
• Develop and manage feature stores, model registries, and automated retraining processes
• Implement monitoring for model performance, data drift, and bias
• Optimize cloud infrastructure for scalable and cost-efficient AI workloads
• Partner with AI Engineers and Scientists to ensure fast, reproducible delivery
• Operationalize LLM agents and multi-agent systems (e.g. containerization, scaling, observability)
• Develop safety, governance, and monitoring frameworks for agentic AI in production

• 5+ years in ML engineering, DevOps, or infrastructure engineering
• Proficiency with MLOps tools (MLflow, Kubeflow, Weights & Biases, Airflow, Dagster, Prefect)
• Strong experience with cloud platforms and container orchestration (AWS/GCP/Azure, Docker, Kubernetes)
• Solid understanding of ML Lifecycles and best practices for reproducibility
• Proven ability to build scalable, automated systems in production
• Experience deploying and monitoring agent frameworks, LLM-based APIs, or AI-augmented workflows preferred