01Overview
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7+ years of IT experience (Data Engineering) with 3+ years in MLOps.
Robust in Python, PySpark, SQL, ML, NLP, Deep Learning .
Expertise in MLOps pipelines MLflow, Apache Airflow.
Cloud experience AWS (SageMaker, Bedrock, EC2, Lambda, S3, Glue) Databricks .
Experience with CI/CD pipelines , automation, and containerization (Docker/Kubernetes).
Knowledge of configuration tools Ansible, Terraform.
End-to-end ML lifecycle: data ingestion, training, deployment, monitoring.
Collaborate with Data Scientists for production-ready solutions.
Skills in model validation, testing, optimization, versioning, monitoring .
Robust in Cloud-native development AI/ML pipeline management .
Positive to have: RNN, CNN, GNN, GAN.
Excellent communication, teamwork, and analytical skills.
Must-Have Skills:
Cloud Services AWS, Azure Services
DevOps Tools Docker, GIT, Jenkins
Machine Learning Mlops, databricks/Jupyter notebook
Infrastructure as a code-Terraform and knowledge on Gen AI
Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying. .
