What are the responsibilities and job description for the Testing not a real job position at SiloSmashers, Inc.?
About The Role
We're looking for a Senior ML Engineer to join a product team and help build, train, and ship machine learning models that power real user-facing features. This is a hands-on role for someone who enjoys owning the full lifecycle of a model - from experimentation through production deployment and monitoring - and who is comfortable working closely with product and engineering partners in a hybrid office environment.
What You'll Do
We're looking for a Senior ML Engineer to join a product team and help build, train, and ship machine learning models that power real user-facing features. This is a hands-on role for someone who enjoys owning the full lifecycle of a model - from experimentation through production deployment and monitoring - and who is comfortable working closely with product and engineering partners in a hybrid office environment.
What You'll Do
- Design, build, and train machine learning models to solve product problems, iterating from prototype to production.
- Own the MLOps lifecycle: experiment tracking, reproducible training pipelines, model versioning, deployment, and monitoring.
- Partner directly with product managers and engineers embedded in your team to translate business requirements into ML solutions.
- Deploy and maintain models on Azure (Azure ML, Azure Databricks, and/or AKS), ensuring reliability and cost efficiency at scale.
- Monitor model performance in production, diagnose drift and degradation, and drive retraining and improvement cycles.
- Write clean, well-tested, production-grade Python code and contribute to shared ML tooling and best practices.
- Collaborate cross-functionally to define success metrics, run experiments (A/B tests), and communicate results to technical and non-technical stakeholders.
- 6–10 years of professional experience in software/ML engineering, with a strong track record of building and training ML models.
- Deep hands-on expertise in Python and common ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn).
- Practical MLOps experience - tools such as MLflow, Kubeflow, or Airflow for pipelines, tracking, and deployment.
- Required: Certified Azure experience (e.g., Microsoft Certified: Azure AI Engineer Associate / AI-102), plus hands-on production experience with Azure ML, Azure Databricks, or AKS.
- Solid understanding of the full ML lifecycle: data preparation, training, evaluation, deployment, and monitoring.
- Strong communication skills and comfort working embedded within a cross-functional product team.
- Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
- Experience with large language models (LLMs) or generative AI APIs (e.g., Claude, OpenAI, Azure OpenAI Service).