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Remote role with broad ML skillset and generic title increases applicant competition despite modest company brand.
ML engineering skills are technical but transferable across industries, requiring domain knowledge but broad applicability.
No explicit years but many required ML tools and deployment skills create moderate shortlisting filters.
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Clean, annotate, and pre-process datasets; implement simple ML models under guidance; produce exploratory data analysis and documentation.
Develop, test, and deploy ML modules including anomaly detection; support data pipelines and model training under supervision.
Potentially lead scalable ML model development, integrate into ITSM systems, and architect end-to-end AI platforms for cross-domain projects like NLP and computer vision.
Bachelor’s degree in Computer Science, Data Science, IT, or related field (Master’s preferred for senior roles).
Work Experience Required: Not explicitly mentioned in the JD.
Proficiency in Python and familiarity with ML frameworks such as TensorFlow, PyTorch, scikit-learn.
Experience with ML pipelines, deployment tools (Flask/FastAPI, CI/CD), cloud ML services, and knowledge of supervised/unsupervised ML methods.
Operates effectively under guidance on ML model development and data processing tasks; capable of advancing towards leading AI platform architecture.
Comfortable working within Agile or DevOps workflows and collaborating across globally distributed teams.
Demonstrates ability to translate business problems into ML solutions and communicate findings to both technical and non-technical stakeholders.