AI Engineers
Remote
Full Time
Mid Level
Role Overview:
We are seeking a skilled AI Engineer with 3–5 years of hands-on experience in designing, developing, and deploying AI/ML solutions in cloud environments. The ideal candidate will have strong proficiency in Python, experience with both Azure and AWS, and a solid understanding of MLOps practices.
Key Responsibilities:
We are seeking a skilled AI Engineer with 3–5 years of hands-on experience in designing, developing, and deploying AI/ML solutions in cloud environments. The ideal candidate will have strong proficiency in Python, experience with both Azure and AWS, and a solid understanding of MLOps practices.
Key Responsibilities:
- Design and implement scalable AI/ML models for banking applications (e.g., fraud detection, credit scoring, customer segmentation).
- Deploy and manage models in production using Azure ML and AWS SageMaker.
- Collaborate with data scientists, software engineers, and DevOps teams to operationalize ML workflows.
- Build and maintain CI/CD pipelines for ML model deployment and monitoring.
- Ensure compliance with data governance, security, and regulatory standards.
- Optimize model performance and resource usage in cloud environments.
- Document processes, models, and deployment strategies for internal knowledge sharing.
- Programming: Strong Python skills, including libraries like Pandas, NumPy, Scikit-learn, TensorFlow or PyTorch.
- Cloud Platforms: Hands-on experience with Azure ML, AWS SageMaker, and cloud-native services (e.g., Lambda, EC2, S3, Azure Functions).
- MLOps: Familiarity with ML lifecycle tools (MLflow, Kubeflow, Airflow), containerization (Docker), and orchestration (Kubernetes).
- Deployment: Experience deploying models as REST APIs or batch jobs in production environments.
- Version Control & CI/CD: Git, GitHub Actions, Azure DevOps, or AWS CodePipeline.
- Monitoring & Logging: Tools like Prometheus, Grafana, or cloud-native monitoring solutions.
- Preferred Qualifications:
- Experience in the banking or financial services domain.
- Knowledge of data privacy regulations (e.g., GDPR, PSD2).
- Exposure to generative AI or LLM fine-tuning is a plus. (Certifications in Azure or AWS) (e.g., Azure AI Engineer Associate, AWS Certified Machine Learning).
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