Key skills
Machine LearningData EngineeringData Pipeline DevelopmentTensorFlowPyTorchScikit-learnGenerative AILarge Language ModelsNatural Language ProcessingModel MonitoringModel VersioningModel RetrainingMLOpsMLflowKubeflowSageMakerVertex AICloud PlatformsAWSGoogle Cloud PlatformOracle CloudAzureContainerizationDockerKubernetesAIMLNLPLLMLlamascikit-learnGCPOracle
About this role
ImpetusIT is seeking a Machine Learning Engineer for its trading client. This role involves designing, developing, and deploying machine learning models, while also maintaining the data infrastructure that supports them.
Responsibilities:
- You’ll be responsible for designing, developing, and deploying machine learning models while also building and maintaining the data infrastructure that powers them
- Build and optimize data pipelines for training and inference workflows
- Develop, train, and deploy machine learning models using frameworks like TensorFlow, PyTorch, or Scikit-learn
- Experience with Gen AI, LLM, LLAMA Index, NLP
- Collaborate with data scientists, analysts, and product teams to define data requirements and model objectives
- Implement model monitoring, versioning, and retraining strategies
- Ensure data quality, lineage, and governance across ML pipelines
- Leverage MLOps tools such as MLflow , Kubeflow , SageMaker , or Vertex AI for model lifecycle management
- Strong understanding of ML lifecycle, from data preprocessing to model deployment
- Experience with cloud platforms (AWS, GCP, Oracle Cloud, or Azure) and containerization (Docker, Kubernetes)
Requirements:
- Experience of 2-3 years
- Designing, developing, and deploying machine learning models
- Building and maintaining the data infrastructure that powers machine learning models
- Building and optimizing data pipelines for training and inference workflows
- Developing, training, and deploying machine learning models using frameworks like TensorFlow, PyTorch, or Scikit-learn
- Experience with Gen AI, LLM, LLAMA Index, NLP
- Collaborating with data scientists, analysts, and product teams to define data requirements and model objectives
- Implementing model monitoring, versioning, and retraining strategies
- Ensuring data quality, lineage, and governance across ML pipelines
- Leveraging MLOps tools such as MLflow, Kubeflow, SageMaker, or Vertex AI for model lifecycle management
- Strong understanding of ML lifecycle, from data preprocessing to model deployment
- Experience with cloud platforms (AWS, GCP, Oracle Cloud, or Azure) and containerization (Docker, Kubernetes)