Senior MLOps Engineer

About the role

AI71 is building an AI‑enabled enterprise platform that embeds intelligence into core operations. As a Senior MLOps Engineer you will design and operate the model serving infrastructure, CI/CD pipelines and observability frameworks that enable reliable, secure and scalable AI workloads for enterprise customers.

Key responsibilities

• Design and build real‑time inference, batch processing and LLM serving layers that meet enterprise latency and availability requirements.
• Develop end‑to‑end CI/CD pipelines for model training, testing, versioning, staged roll‑outs and rollback mechanisms.
• Establish a comprehensive observability stack to monitor performance, model drift, data quality, cost efficiency and output quality.
• Own evaluation infrastructure, including regression testing for non‑deterministic AI outputs.
• Implement infrastructure‑as‑code across all environments while adhering to security and compliance standards.
• Collaborate with data scientists and ML engineers to streamline the transition from notebook experiments to production deployments.

Required profile

• Significant hands‑on experience running machine‑learning systems in production with a focus on platform engineering.
• Deep expertise in Kubernetes and container orchestration, including cluster‑level architecture decisions.
• Proven track record of building CI/CD pipelines and using Terraform (or equivalent) for infrastructure as code.
• Strong software engineering skills in Python for production‑grade code.

Required skills

• Kubernetes, Docker, containerization
• Terraform (or equivalent IaC)
• CI/CD pipeline tools (e.g., GitLab CI, Jenkins)
• Model serving frameworks: KServe, Seldon, BentoML, vLLM, Triton
• Python
• ML lifecycle tools: MLflow, Kubeflow, Airflow, feature stores
• Observability: Prometheus, Grafana
• Cloud platforms: AWS, Azure, GCP (or sovereign clouds)