AI DevOps

Summary

Join Leadtech's AI Lab as an AI DevOps Engineer and play a key leadership role in establishing scalable workflows and infrastructure for managing AI models. You will design and implement CI/CD pipelines, automate deployments, optimize infrastructure usage, and collaborate with cross-functional teams. This role requires strong expertise in MLOps, cloud infrastructure, and automation. Leadtech offers a flexible work environment, competitive salary, comprehensive benefits, and opportunities for growth and development. Enjoy a flexible schedule, remote work options, and unique perks in our Barcelona office. Leadtech is an equal opportunity employer and values diversity.

Requirements

• Proven experience as a DevOps Engineer, preferably with exposure to AI/ML workflows and production environments
• Strong knowledge of CI/CD pipelines and best practices for automated deployment in cloud environments
• Hands-on experience with containerization and orchestration tools, particularly Docker and Kubernetes
• Proficiency in cloud infrastructure management, with the ability to optimize resource usage and costs while maintaining performance and scalability
• Expertise in monitoring tools such as Prometheus, Grafana, or the ELK stack, and log management systems
• Proficiency in scripting and automation using Python, Bash, or similar languages
• Deep understanding of version control systems and branching strategies (e.g., Git)
• Knowledge of security best practices in DevOps pipelines and cloud environments, including data encryption, access controls, and vulnerability management
• Ability to collaborate with cross-functional teams, including operations, data science, and business teams, to ensure seamless model integration and alignment with business goals

Responsibilities

• Design, implement, and manage scalable CI/CD pipelines for the deployment and lifecycle management of AI models and related applications
• Conduct technical investigations of AI models, focusing on both inputs and outputs, evaluating performance, scalability, and cost-efficiency by comparing resource consumption (e.g., GPU, CPU, token usage). The engineer will also be responsible for testing models iteratively in sprints for internal demos via Scrum to ensure they meet business requirements and are production-ready
• Automate the deployment, monitoring, and maintenance of AI-powered applications in production environments, ensuring scalability, reliability, and performance
• Continuously improve workflows by implementing best practices in automation, orchestration, and monitoring, ensuring that AI models are efficiently integrated into production systems
• Optimize infrastructure usage and manage costs, ensuring that cloud resources are used effectively without compromising performance or scalability
• Implement containerization and orchestration strategies using tools such as Docker and Kubernetes to ensure scalable and fault-tolerant deployments
• Establish and enforce security best practices for AI-centric workloads, including data encryption, access controls, and vulnerability management
• Collaborate with cross-functional teams, including data scientists, software developers, and business teams, to ensure that AI models are integrated seamlessly into production workflows and meet business needs
• Take ownership of AI model demonstrations, preparing and showcasing how models perform in production to Product Owners (POs) during internal sprints
• Develop and implement logging, monitoring, and alerting frameworks to ensure system reliability and uptime for AI services
• Participate in incident response processes, troubleshooting and resolving issues related to AI pipelines and infrastructure
• Stay updated on industry trends, tools, and best practices in DevOps, MLOps, and AI infrastructure to drive continuous improvement

Preferred Qualifications

• Strong communication skills, with the ability to lead meetings involving stakeholders from diverse backgrounds, ensuring clear alignment between technical and non-technical teams
• Experience engaging with external vendors and stakeholders, demonstrating the ability to ask the right technical and business questions to gather the necessary information for decision-making, and create reports that clearly communicate the big picture to business stakeholders, helping them understand the impact and value of AI solutions
• Problem-solving mindset and proactive approach to reduce downtime and optimize model operations based on performance and cost metrics
• Experience in working within Scrum frameworks, contributing to sprints by iteratively improving AI models and ensuring they are ready for internal demos and production deployment
• Strong troubleshooting skills, with the ability to quickly diagnose and resolve issues in AI pipelines and infrastructure
• Experience with MLOps frameworks such as MLflow, Kubeflow, or similar tools for model training, versioning, and retraining in production

Benefits

• Growth and career development
• Personalized internal training and an annual budget for external learning opportunities
• Work-Life balance
• Flexible schedule with flextime (7 - 9:30 a.m. start, 3:30 - 6 p.m. end) and the option of working full remote or from our Barcelona office
• Free Friday afternoons with a 7-hour workday, plus a 35-hour workweek in July and August so you can savor summer!
• Competitive salary, full-time permanent contract, and top-tier private health insurance (including dental and psychological services)
• 25 days of vacation plus your birthday off, with flexible vacation options—no blackout days!
• Free coffee, fresh fruit, snacks, a game room, and a rooftop terrace with stunning Mediterranean views
• Ticket restaurant and nursery vouchers, paid directly from your gross salary