Role & responsibilities

Hands-on experience with Azure ML Building, deploying, and managing ML pipelines. / Model testing

• Python scripting or programming experience. (At least to write any automation notebook etc)

- Observability experience Azure Monitor for logs, metrics, and traces. (OTel)
- Strong IaC knowledge Proficient in Terraform, Pulumi, and Bicep for cloud infrastructure automation.
- CI/CD for ML Models – Experience with GitHub Actions for model deployment/infra deployment.
- Basic understanding of DevOps / MLOps / AIOps principles.
- Some understanding of ML lifecycle and monitoring would help
- Cloud and Security Best practices awareness
- Ability to explain the technical work to both tech and non-tech stakeholders
- Programming in any of the following: Python, R, Java, JavaScript, GO or similar
- Working experience of Architecture implementation on end-to-end operations of Machine learning projects from scratch
- MLOps experience including Linux and Windows command line control, scripting, source code control (such as GitHub), containers (including Docker, Kubernetes) and orchestration tools
- Experience in different tools such as serverless functions, DevOps tools to build training and inference pipelines, Monitoring or similar

• Data Storage Technologies: SQL, NoSQL, Hadoop, cloud-based databases such as GCP BigQuery, and different storage formats (e.g. Parquet, etc.)





• Data Processing Tools: Python (Numpy, Pandas, etc.), Spark, cloud-based solutions such as GCP DataFlow

• Machine Learning Libraries: Python (scikit-learn, genism, etc.), TensorFlow, Keras, PyTorch, Spark MLlib; Cloud certifications are a plus • Demonstrated ability to create end-to-end technology prototypes and/or machine learning models for a given business use case or application.

• Demonstrated experience with rapid prototyping, using agile approaches to quickly test new ideas and fail rapid.

• Demonstrated ability to apply a business framing to emerging technology solutions and communicate to business audiences in written and verbal formats.

• Things, Virtual Reality, Augmented Reality, and Robotics.

Qualifications:

• Bachelor’s degree in computer science, Information Technology, or related field.

• Proven experience as a ML Engineer or similar role.

• Strong problem-solving skills and attention to detail.

• Ability to work both independently and as part of a team.

• Excellent communication and interpersonal skills.

Preferred Qualifications:

• Experience with cloud platforms, particularly Azure.

• Familiarity with additional machine learning libraries such as TensorFlow, scikit-learn, or PyTorch.

• Certification in RPA or machine learning technologies is a plus.

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