AI / ML Ops Engineer

About the role

We are seeking an experienced AI / ML Ops Engineer to bridge the gap between data science research and cloud infrastructure execution. The ideal candidate will build automated pipelines to train, test, deploy, and monitor machine learning models and generative AI systems securely and at scale within enterprise container environments.

What you'll do

• Design and automate end-to-end ML pipelines (Continuous Training and Continuous Deployment) using orchestration engines like Kubeflow , MLflow
• Implement robust model tracking and data versioning foundations , managing feature stores (e.g., Feast), model registries
• Build automated AI performance and data monitoring gates , tracking model accuracy decay, data drift indicators, concept drift parameters
• Optimize inference execution environments , leveraging model compilation engines (e.g., ONNX
• Integrate generative AI and LLM operational frameworks (LLMOps) , configuring semantic caching layers, vector database scaling parameters (e.g., Pinecone, Milvus)

What you need

• MLOps
• Machine learning
• SQL
• Python
• AWS
• Databricks

Good to have

• Terraform
• PyTorch
• TensorFlow
• GCP
• LLMs
• Kubernetes