AWSDockerKerasKubernetesPySparkPythonPyTorchScikit-LearnTensorflowTypeScriptAIMachine LearningMLGenerative AILarge Language ModelsRAGAgenticTensorFlowscikit-learnMLOpsGitLab CISageMakerBedrockGitLabSource ControlJiraConfluenceCI/CD
About this role
Role Overview
Implement Machine Learning Operations (MLOps) practices to automate and standardize the ML lifecycle—from model development and training to deployment and monitoring
Apply DevOps principles to ensure reliable, efficient, and scalable ML systems in production
Design and develop AI/ML pipelines supporting: Machine Learning models, Generative AI and Large Language Models (LLMs), Retrieval Augmented Generation (RAG) architectures, Agentic AI workflows
Translate stakeholder requirements into functional prototypes and deployable enterprise solutions
Support model performance monitoring, optimization, and continuous improvement
Develop AI/ML solutions using Python and frameworks such as TensorFlow, PyTorch, Scikit-learn, Keras, PySpark, vLLM, NVIDIA CUDA
Manage source control using GitLab
Implement CI/CD pipelines using GitLab CI/CD
Utilize containerization and orchestration technologies including Docker and Kubernetes
Requirements
Bachelor’s degree and 11+ years of relevant experience (or equivalent combination of education and experience)
Strong experience building and deploying AI/ML models in production environments