MLOps Engineer

Description At Sequoia Connect , we are a Talent-First Technology Ecosystem that redefines how elite professionals interact with the global digital landscape. We move beyond traditional models to act as a catalyst for the top 1% of global talent, connecting human potential with complex industrial execution. By joining our inner circle, you are not simply taking a position; you are aligning with a strategic partner dedicated to updating your "Human OS" and accelerating your growth through world-class, high-impact projects. We are currently partnering with a global IT powerhouse that represents the connected world through innovative, customer-centric experiences. As a USD 6 billion organization and one of the top 7 IT service providers globally, our client empowers over 1,200 global customers—including several Fortune 500 companies—to "Rise™." With a massive network of 163,000+ professionals across 90 countries, they are at the absolute forefront of digital transformation, leveraging next-generation technologies such as 5G, AI, Blockchain, and Quantum Computing. This is your chance to thrive in a workplace recognized as one of the most sustainable corporations in the world. You will join an environment that values innovation and societal impact, working on end-to-end digital transformation projects for global leaders. If you are a driven professional looking for global career opportunities and exposure to high-impact projects within an international network of expertise, this is where you belong. We are currently searching for a MLOps Engineer / ML Platform Engineer: The Challenge (Responsibilities) Monitor AI models and agents in production for performance, latency, errors, and availability, tracking statistical health indicators such as model drift and data distribution changes. Detect and triage production incidents related to AI behavior, executing rollbacks, throttling, or model disabling where thresholds are breached. Support deployment, versioning, and release of AI models and agents using CI/CD-style pipelines and maintain registries covering model ownership and lineage. Ensure AI systems adhere to Responsible AI principles, maintaining audit trails and supporting fairness, bias, explainability, and transparency monitoring in production. Integrate AI systems with monitoring, logging, and alerting platforms, collaborating with product, engineering, and data teams to standardize AI Ops patterns. Your Profile (Requirements) Strong Python skills and experience supporting ML or LLM-based systems. Deep understanding of Model Ops / MLOps, focusing on the operational phase after deployment. Experience with monitoring and logging systems, CI/CD pipelines, and containerized deployments (e.g., Docker-based runtimes). Ability to work cross-functionally with product, data science, engineering, and risk teams. High-Performance Mindset: Resilience, emotional intelligence, and a focus on agile delivery. Technologist DNA: A deep understanding of the difference between "coding" and "engineering." Desired Familiarity with cloud platforms (Azure preferred) and production troubleshooting. Familiarity with cloud-native foundations or AI coding assistants. Languages Advanced Oral English: For seamless collaboration with global teams. Advanced Spanish. Work Arrangement We value flexibility to support your lifestyle. This position is available as: Remote If you meet these qualifications and are pursuing new challenges, start your application on our website to join an award-winning employer. Explore all our job openings | Sequoia Career’s Page : https://www.sequoia-connect.com/careers/ Requirements Strong Python skills and experience supporting ML or LLM-based systems; Understanding of Model Ops / MLOps, especially the operational phase after deployment; Experience with monitoring and logging systems, CI/CD pipelines, and containerised deployments (e.g. Docker-based runtimes); Familiarity with cloud platforms (Azure preferred) and production troubleshooting; Ability to work cross-functionally with product, data science, engineering, and risk teams; Experiencie with IA.