Senior MLOps Engineer

Later is the world’s most intelligent influencer marketing company. Built on a native, AI-powered platform and more than a decade of proprietary data, including billions of social interactions and $2.4B+ in verified influencer-driven purchases, Later helps brands choose the right creators, run fully managed campaigns, and know what will work before they launch.

We’re looking for a Senior MLOps Engineer to build and scale the infrastructure that takes Later’s machine learning and AI systems from prototype to production. You’ll own the pipelines that train, retrain, deploy, and serve the models behind our products, including forecasting, recommendation, and LLM-based and agentic features, across AWS and GCP.

Strategy & Architecture

• Own the MLOps roadmap with Infrastructure, Data Science, and Data Engineering leaders, aligned to product and AI growth plans.
• Evaluate and select tooling for orchestration, model registry, experiment tracking, LLM serving, and observability, and make clear build-or-buy recommendations.
• Shape the long-term architecture of our multi-cloud ML and data platform, with scalability, security, and cost built in from the start.

Building & Operating

• Design, build, and operate infrastructure that deploys and scales ML models in production, using CI/CD, GitOps, Kubernetes, and orchestration tools such as Airflow, Kubeflow, or Argo Workflows.
• Own automated training and retraining pipelines, including model registry, validation, deployment, and rollback, and agree on the retraining cadence with Data Science.
• Deploy and operate LLM inference (self-hosted on AWS Bedrock or managed APIs), including GPU capacity, cost controls, and performance monitoring.
• Manage training and inference infrastructure across AWS and GCP, including GPU workloads, using Terraform.
• Work with Data Engineering to source and prepare training data from BigQuery and S3, building pipelines and light transformations as needed.
• Build monitoring for model performance, latency, data drift, cost, and resource usage, using tools such as CloudWatch, Prometheus, and Grafana.
• Support model versioning, experiment tracking (e.g., MLflow, SageMaker), and reproducible training workflows.
• Lead troubleshooting of complex production and deployment issues, and take part in the Infrastructure on-call rotation.

Leadership & Collaboration

• Set and document standards for infrastructure-as-code, CI/CD, observability, secure deployment, and ML workflow automation.
• Write runbooks and self-serve guides so Product and Data teams can deploy, monitor, and troubleshoot with more autonomy.
• Lead incident reviews and post-mortems that improve how we operate.
• Mentor engineers through pairing, code review, and feedback.
• Explain technical tradeoffs clearly to both technical and non-technical stakeholders.

What success looks like First 30 days: Learn and support. You learn Later’s cloud infrastructure, Kubernetes environments, CI/CD workflows, and the data and ML platform, including how models are trained and deployed today. You start supporting existing pipelines and observability tools. By day 30 you know where the platform’s biggest gaps are and how Product and Data teams use it.

First 60 days: Contribute and improve. You own infrastructure and automation work across Kubernetes, AWS, CI/CD, and the ML platform. You make deployment pipelines more consistent and automated, improve GPU provisioning and monitoring for training and inference, and close gaps in alerting and runbooks. You’re already mentoring teammates on the patterns you’re introducing.

First 90 days: Own and scale. You own the path from a finished model to production. By day 90:

• At least one production model is deployed, retrained, and rolled back through an automated pipeline, with no manual steps.
• Drift, latency, and cost alerts are live for production models.
• A first MLOps roadmap has been reviewed with Infrastructure, Data Science, and Data Engineering leads.
• Security and compliance checks are built into the ML deployment path.

Requirements

• 7+ years of DevOps, cloud, or software engineering experience in production environments, including technical leadership: leading projects, setting direction, and mentoring others.
• Deep expertise with Kubernetes (EKS), Helm, and microservices, including running GPU workloads on Kubernetes.
• Hands-on AWS experience and a track record of using Terraform for scalable, auditable infrastructure as code.
• Experience deploying, serving, and scaling ML models in production, including CI/CD for ML.
• Experience building data and training pipelines in partnership with Data Science and Data Engineering teams.
• Strong system design skills, including designing ML systems end to end and explaining the tradeoffs.
• Clear communication with technical and non-technical audiences.

Nice to have

• GCP experience, especially BigQuery.
• Amazon SageMaker.
• Self-hosted LLM deployments on Kubernetes (e.g., vLLM, KServe).
• Orchestration and experiment tracking tools such as Airflow, Kubeflow, Argo Workflows, or MLflow.

How you work

• Driven by Impact: You deliver results that matter,prioritizing high-value work, meeting deadlines, and adapting quickly while keeping outcomes clear.
• Strategic & Customer-Centric: You anticipate risks and opportunities, connect decisions to long-term growth, and build trust through proactive insights.
• Curious & Growth-Oriented: You seek knowledge, ask sharp questions, and apply learnings fast,challenging the status quo with a mindset of improvement.
• Collaborative & Resilient: You thrive in change by staying resourceful, solution-focused, and positive,removing roadblocks, sharing insights, and keeping morale high.
• Accountable & Honest: You own your work, hold yourself and others to a high bar, and use transparent feedback to drive growth.
• Emotionally Intelligent: You build trust through empathy and collaboration, foster inclusion, and inspire others with grit, optimism, and integrity.

Compensation

• Salary range: For candidates located in the US: $125,000-205,000 USD
• For candidates located in Canada: $110,000-160,000 CAD