ML Ops Engineer

About UsAt Félix, we're building the financial ecosystem for Latin immigrants in the U.S., starting with a revolution in remittances. Our core product is an AI-powered chatbot built on WhatsApp, allowing our users to send money home as easily as sending a text message. We leverage cutting-edge technology like AI, blockchain, and stablecoins to make cross-border payments faster, more affordable, and more accessible than ever before.We are a hyper-growth Series B company, backed by over $100 million in funding from top-tier global investors, including QED, Castle Island, Switch Ventures, HTwenty, Monashees, and General Catalyst Customer Value Fund. This isn't just about the numbers; it's a testament to the trust our investors have in our vision and our team. Additionally, Félix was selected as an “Endeavour Entrepreneur” and was a recipient of the CrossTech Fintech Startups Award. We are a group of extremely talented and dedicated high-performers, united by our shared obsession with a single goal: empowering our customers. We are all owners of Félix, driven by a bias for action and a true experimentation spirit to get shit done with urgency and focus.Joining Félix means you will be part of a team building a legacy, a company that will outlive us all. This is a rare opportunity to apply your skills to a deeply meaningful mission—serving a community that has been underserved for too long. We are a team that is fiercely loyal to each other, where radical transparency and constructive feedback are how we grow and push for excellence. We are bold, we care less about what others are doing, and more about creating sustainable value and a product that truly makes our users' lives better. We are building the future, today.About The RoleAs a foundational MLOps Engineer at Félix, you will be responsible for building, scaling, and securing the infrastructure that powers our machine learning initiatives. You will partner with the AI Squad and Data Scientists to productionize their models, transforming them from research artifacts into reliable, high-availability services.This is a green-field opportunity to build our MLOps practice from the ground up, focusing on automation, reliability, and security from day one. You will be working directly with Damian Finol, our Head of Engineering Operations, in close partnership with DevOps and SecOps to ensure our ML platform integrates seamlessly with our existing GKE/Helm-based infrastructure.ResponsibilitiesBuild & own the ML lifecycle: Design, build, and maintain our end-to-end ML platform, including data ingestion pipelines, feature stores, model training environments, and model serving infrastructure.CI/CD for models (CI/CT/CD): Implement robust CI/CD pipelines for ML models, including automated testing (unit, integration, and data validation), continuous training (CT), and safe, progressive deployment (e.g., canary, shadow) of new model versions.Production monitoring & reliability: Go beyond basic service health. Implement sophisticated monitoring for our production models to track performance, detect data/concept drift, and measure business KPIs. Build the alerting and automated rollback mechanisms to protect our services.Partner with DevOps & SecOps: Integrate the ML platform seamlessly with our existing GKE/Helm-based infrastructure. Partner with SecOps to secure our ML data, pipelines, and endpoints against adversarial attacks and data leakage.Enable Data Scientists: Build the "paved path" for the AI Squad. Create self-service tools, templates (e.g., Helm charts, Dockerfiles), and documentation that let them train and deploy models safely without being infrastructure experts.RequirementsStrong DevOps/SRE foundation: 5+ years in a DevOps, SRE, or Platform Engineering role with deep, hands-on experience in:Kubernetes: Production experience with GKE is a must. You should be able to manage K8s workloads, understand networking, and write your own Helm charts.CI/CD: Expertise in building and managing pipelines (e.g., GitHub Actions, ArgoCD).IaC: Proficiency with Terraform or similar tools.ML infrastructure experience: 2+ years of hands-on experience building and managing MLOps infrastructure. This must include:Experience with a modern MLOps stack (e.g., Kubeflow, Vertex AI, MLflow, Seldon Core).Proven experience deploying and monitoring real-time ML models (e.g., REST APIs) in a high-availability environment.Strong programming skills: Proficiency in Python for scripting, automation, and data pipeline development.Data & security mindset: Experience with datastores (BigQuery), data pipelines (e.g., Airflow, GCP Dataflow), and a strong understanding of data governance, PII handling, and infrastructure security principles.Bonus: Experience in regulated environments (Fintech, Payments, or Healthtech); streaming data technologies (e.g., Kafka, Pub/Sub); and common ML frameworks (TensorFlow, PyTorch, scikit-learn) and what it takes to run them at scale.These are the applicable requisites, although equivalent competencies in any of the above will also be considered.What We OfferCompetitive salaryInitial stock options grantAnnual performance bonusHealth, dental, and vision plans Remote work environment, although we have offices in Miami and México City and would love to work in hybrid model if you are up to it.Continuous learning opportunities Unlimited PTOPaid parental leaveEmpowering opportunities for growth in a dynamic entrepreneurial environmentEqual Opportunity EmployerAt Félix, we are committed to providing equal employment opportunities to all qualified employees and applicants without regard to race, religion, nationality, sex, sexual orientation, gender identity, age, or disability. 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