MLOps Engineer

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.