ML Ops Engineer

About Us At 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 users to send money home like sending a text message. We leverage cutting‑edge technology such as 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. Félix was selected as an “Endeavour Entrepreneur” and received the CrossTech Fintech Startups Award. We are a team of highly dedicated high‑performers, united by a single goal: empowering our customers and building a legacy that will outlive us all. About The Role As 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 green‑field opportunity allows you to build our MLOps practice from the ground up, focusing on automation, reliability, and security from day one. You will work directly with Damian Finol, 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. Responsibilities Build & 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: Implement sophisticated monitoring for our production models to track performance, detect data/concept drift, and measure business KPIs. Build 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. Work 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. Requirements Strong 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. Manage K8s workloads, understand networking, and write 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, including: Experience with a modern MLOps stack (e.g., Kubeflow, Vertex AI, MLflow, Seldon Core). Proven experience deploying and monitoring real‑time ML models (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 (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 (Kafka, Pub/Sub); and common ML frameworks (TensorFlow, PyTorch, scikit‑learn) and what it takes to run them at scale. Equivalent competencies in any of the above will also be considered. What We Offer Competitive salary Initial stock options grant Annual performance bonus Health, dental, and vision plans Remote work environment; hybrid model possible for Miami and México City locations Continuous learning opportunities Unlimited PTO Paid parental leave Empowering opportunities for growth in a dynamic entrepreneurial environment Equal Opportunity Employer At 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. This policy applies to all terms and conditions of employment, including recruitment, hiring, placement, promotion, training, compensation, benefits, and termination. Seniority Level Mid‑Senior level Employment Type Full‑time Job Function Engineering and Information Technology #J-18808-Ljbffr