AI Engineer - Machine Learning

We are seeking a skilled AI Engineer with 3-8 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.

Required Skills :

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 Code Pipeline.

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).