
1 - 10 employees
Founded 2017
🎯 Recruiter
👥 HR Tech
☁️ SaaS
Recruitment • HR Tech • SaaS
SkillTude Talent Solutions is an AI-powered recruitment platform that connects exceptional tech talent with innovative companies through machine-learning matching, ATS optimization, and automated hiring workflows. It provides end-to-end recruitment services including free professional CV analysis, personalized candidate experiences, and specialized hiring across 16 technology sectors, serving startups and enterprise clients.
🕒 October 24, 2025
Airflow
AWS
Azure
Cloud
Docker
EC2
Grafana
Kubernetes
Numpy
Pandas
Prometheus
Python
PyTorch
Scikit-Learn
Tensorflow
Improve your chances of getting an interview by checking your resume score before you apply.

1 - 10 employees
Founded 2017
🎯 Recruiter
👥 HR Tech
☁️ SaaS
Recruitment • HR Tech • SaaS
SkillTude Talent Solutions is an AI-powered recruitment platform that connects exceptional tech talent with innovative companies through machine-learning matching, ATS optimization, and automated hiring workflows. It provides end-to-end recruitment services including free professional CV analysis, personalized candidate experiences, and specialized hiring across 16 technology sectors, serving startups and enterprise clients.
• 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.
• 5+ years of experience • Strong Python skills, including libraries like Pandas, NumPy, Scikit-learn, TensorFlow or PyTorch. • Hands-on experience with Azure ML, AWS SageMaker, and cloud-native services (e.g., Lambda, EC2, S3, Azure Functions). • Familiarity with ML lifecycle tools (MLflow, Kubeflow, Airflow), containerization (Docker), and orchestration (Kubernetes). • Experience deploying models as REST APIs or batch jobs in production environments. • Git, GitHub Actions, Azure DevOps, or AWS CodePipeline. • Tools like Prometheus, Grafana, or cloud-native monitoring solutions. • Experience in the banking or financial services domain. • Knowledge of data privacy regulations (e.g., GDPR, PSD2).
Apply Now🕒 October 24, 2025
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