About Us

At Cabernet AI, we are revolutionizing the way Investment Banks and Private Equity firms find and execute deals. We help these firms gain insights faster and act more decisively using cutting-edge AI-powered applications. As we scale our operations, we are looking for an experienced Data Infrastructure Engineer to join our team and help us build robust, scalable, and efficient systems that bring our AI to our customers.

What You’ll Do As a Senior Data Infrastructure Engineer, you will play a pivotal role in designing, deploying, and maintaining the infrastructure that powers our AI-driven solutions. Your work will directly influence how our models are developed, tested, deployed, and monitored. You'll collaborate closely with data scientists, backend developers, and business teams to ensure the smooth operation of our ML pipelines, as well as the performance of models in production.

• Key Responsibilities Cloud Infrastructure: Design and maintain scalable, secure, and cost-efficient cloud architectures.
• MLOps & CI/CD: Develop and manage CI/CD pipelines to streamline the deployment of machine learning models. Automate the full lifecycle of ML workflows, ensuring smooth transitions from development to production.
• Backend Development: Work on building scalable backend systems, APIs, and microservices that drive our AI products.
• Containerization & Orchestration: Leverage Docker and container orchestration platforms (e.g., AWS ECS) to ensure that our applications are scalable and easy to manage.
• Data Engineering: Build, optimize, and manage ETL pipelines that support large scale data ingestion and processing.
• Machine Learning Deployment: Work closely with our data scientists to bring machine learning models (both traditional and NLP focused) from research to production, ensuring proper versioning, testing, and monitoring.
• Model Monitoring & Support: Set up systems for continuous monitoring and performance tracking of models in production. Ensure models are regularly updated, and the infrastructure supports iterative development and A/B testing.
• Collaboration & Iteration: Design infrastructure that enables seamless collaboration between engineers and data scientists for quick POCs and smooth production rollouts.

What We’re Looking For

• Proven experience with cloud platforms like AWS, GCP, or Azure, and the ability to architect and maintain scalable, cost efficient cloud infrastructure.
• Expertise in MLOps tools and practices, including setting up CI/CD pipelines for machine learning models (we use Github Actions).
• Strong skills in backend development, particularly in building microservices and RESTful APIs.
• Proficiency in Docker and container orchestration tools (e.g., ECS).
• Experience working with data pipelines and ETL processes for large scale datasets.
• Knowledge of machine learning deployment workflows, including NLP and traditional ML models.
• Ability to set up and manage model monitoring systems to ensure performance, scalability, and ongoing optimization.
• Excellent collaboration skills, particularly in working alongside data scientists to take models from proof of concept to production.

Why Join Us?

• Be a part of a high-growth, dynamic startup that’s at the forefront of AI innovation for investment banking and private equity.
• Work with a team of passionate engineers, data scientists, and business professionals.
• Opportunities for personal and professional growth in a fast moving environment.
• Competitive salary, equity options, and flexible remote work arrangements.