Overview
The Machine Learning Engineer (LLMOps) builds and operates the infrastructure that takes AI models from experiment to reliable, scalable production.
This role ensures models are deployed, monitored, and continuously improved with rigorous engineering discipline.
Your Role and Responsibilities
What You’ll Do
Key Responsibilities
- Build and maintain ML/LLM pipelines for training, evaluation, and deployment.
- Operate model-serving infrastructure with high availability and low latency.
- Implement monitoring, logging, and drift detection for models in production.
- Automate CI/CD for models, prompts, and datasets.
- Optimize inference cost and performance across cloud environments.
- Collaborate with data scientists and AI engineers to productionize their work.
REQUIREMENTS
What This Role Requires
Education
- Bachelor’s degree in Computer Science, Engineering, or a related field.
Experience
- 4+ years in software or ML engineering, with 2+ years in MLOps/LLMOps or platform work.
- Experience running ML workloads in production.
Technical Skills
- Strong Python and infrastructure-as-code (Terraform) skills.
Experience with containers and orchestration (Docker, Kubernetes). - Familiarity with ML platforms (SageMaker, Vertex AI, MLflow, Kubeflow).
- Knowledge of model serving, GPU optimization, and observability tooling.
- Understanding of data pipelines and feature stores.
Soft Skills
- Strong operational and reliability mindset.
- Methodical, detail-oriented problem-solving.
- Effective cross-team collaboration.
- Proactive about automation and continuous. improvemen
Working at Motivus
What Working Here Looks Like
- Guild community: You’re part of the [Data & AI / Backend / Cloud] Guild — a community of practitioners in your domain. Structured learning, peer review, and certifications happen here, not in isolation.
- Outcome accountability: Your work connects directly to a client result. We don’t measure activity — we measure delivery.
- AI in your workflow: Through AI Champions and Guild initiatives, you’ll actively use and shape how AI tools are applied to engineering work — not just hear about it in a townhall.
- Defined career path: Structured progression from individual contributor to technical lead to Guild leadership, with certifications and milestones that are visible, not arbitrary.
