Senior MLOps / AI Infrastructure Engineer
New
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SmartRecruitment.comAI infrastructure
Source API remote eligibility restrictions: Brazil, Malta, TaiwanFull-TimeSenior
Salary not disclosed
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Job Details
- Languages
- Proficiency in English; Mandarin is a plus.
- Experience
- 4+ years of software engineering experience, with at least 2 years in ML infrastructure or DevOps for AI/ML.
- Required Skills
- AWSDockerPythonGCPKubernetesMLFlowPyTorchGoRustTensorflow
Requirements
- Bring 4+ years of software engineering experience, including at least 2 years in ML infrastructure or AI/ML DevOps.
- Be proficient in Python and one systems language, such as Go or Rust.
- Have strong expertise in Kubernetes, Docker, and cloud infrastructure; GCP is preferred.
- Be familiar with ML tooling such as PyTorch, TensorFlow, MLflow, or Ray.
- Have experience deploying ML models in production at scale, preferably in containerized environments.
- Understand distributed systems, resource orchestration, and observability.
- Have proficiency in English; Mandarin is a plus.
- Experience serving large foundation models, exposure to RAG, vector search, or open-source model fine-tuning, and familiarity with Terraform or Helm are nice to have.
Responsibilities
- Build and scale core ML infrastructure for distributed training, hyperparameter tuning, and experiment tracking.
- Design and maintain containerized, low-latency model-serving infrastructure for LLMs and multimodal models.
- Develop CI/CD pipelines for machine-learning workflows using tools such as MLflow, Airflow, or Kubeflow.
- Optimize compute usage and resource allocation on GCP or AWS and Kubernetes clusters.
- Implement observability and alerting for model performance, drift, and production uptime.
- Collaborate with ML researchers, software engineers, and product teams to productionize research models.
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