Your mission
Your Mission
As a Senior ML/AI Engineer at Astrafy, you will lead the design, deployment, and scaling of
ML/AI models in production. You'll work cross-functionally with both technical and business
teams to craft tailored, high-impact AI solutions that drive real-world value.
This is a high-ownership role where you'll act as both a technical expert and a strategic
thinker — ensuring delivery of robust, scalable models while aligning closely with
stakeholder needs and business KPIs.
On the Business Side
- Partner with clients and internal stakeholders to understand strategic objectives and
uncover ML/AI opportunities.
- Assess feasibility by analyzing available data sources and proposing realistic and impactful
solutions.
- Define project goals, success criteria, and measurable KPIs in alignment with business
needs.
On the Technical Side
- Lead the design and development of machine learning models using PyTorch, TensorFlow,
or similar frameworks.
- Implement best-in-class MLOps practices to automate the training, deployment, and
monitoring of models (e.g., with Kubeflow).
- Build and optimize scalable pipelines for data ingestion, model training, evaluation, and
inference.
- Ensure full automation from preprocessing to production, with robust testing and CI/CD
integration.
- Collaborate closely with data engineers and platform teams to ensure model performance,
reliability, and maintainability.
As a Senior ML/AI Engineer at Astrafy, you will lead the design, deployment, and scaling of
ML/AI models in production. You'll work cross-functionally with both technical and business
teams to craft tailored, high-impact AI solutions that drive real-world value.
This is a high-ownership role where you'll act as both a technical expert and a strategic
thinker — ensuring delivery of robust, scalable models while aligning closely with
stakeholder needs and business KPIs.
On the Business Side
- Partner with clients and internal stakeholders to understand strategic objectives and
uncover ML/AI opportunities.
- Assess feasibility by analyzing available data sources and proposing realistic and impactful
solutions.
- Define project goals, success criteria, and measurable KPIs in alignment with business
needs.
On the Technical Side
- Lead the design and development of machine learning models using PyTorch, TensorFlow,
or similar frameworks.
- Implement best-in-class MLOps practices to automate the training, deployment, and
monitoring of models (e.g., with Kubeflow).
- Build and optimize scalable pipelines for data ingestion, model training, evaluation, and
inference.
- Ensure full automation from preprocessing to production, with robust testing and CI/CD
integration.
- Collaborate closely with data engineers and platform teams to ensure model performance,
reliability, and maintainability.