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Remote ML role with generalist ML expectations increases candidate density.
Deep ML, MLOps, and domain-specific expectations limit cross-industry transferability.
Explicit level bands and mandatory technical skills (Python, ML frameworks) enforce moderate filtering.
Lead end-to-end machine learning lifecycle including prototyping, deployment, and monitoring of models with a focus on real-time production environments.
Develop and maintain observability tools and dashboards to track ML metrics and feature drift ensuring model performance and reliability.
Architect and implement AI/ML solutions and governance that align with business goals and enterprise-wide AI strategy, driving innovation and cross-functional collaboration.
Work Experience Required: 1-2 years (Level 1/2) to 10+ years (Level 5) in machine learning, AI implementation, or software engineering with increasing leadership responsibility depending on level.
Mandatory Technical Skills: Expertise in Python is required; experience with ML frameworks such as TensorFlow, PyTorch, and scikit-learn; knowledge of cloud ML platforms (AWS SageMaker, GCP Vertex AI, Azure ML).
Familiarity with MLOps tools and practices including Docker, Kubernetes, CI/CD, MLflow, and Airflow.
Preferred Certifications: Certifications in cloud ML engineer roles (Google Cloud, AWS, Azure), TensorFlow Developer, Kuberenetes/Docker for MLOps roles preferred.
Experienced in designing and delivering scalable, real-world AI/ML solutions involving IT operations or security domains with responsibility over AI product lifecycles.
Demonstrated capability to lead AI strategy, governance, and innovation within complex, cross-functional environments and align AI initiatives with business and compliance objectives.
Strong ability to communicate complex AI/ML concepts to technical and non-technical stakeholders and to drive organizational adoption of emerging ML technologies and ethical standards.