Senior ML Engineer
4y relevant experience
EU engineers, ready to place with your US clients
Pre-screened on AI. Remote B2B contracts. View 5 full profiles free — AI score, skills report, interview questions included.
Executive Summary
Strong senior-level candidate with 4+ years of production ML experience at SAP, demonstrating expertise in MLOps, cloud infrastructure, and model deployment. Has successfully led teams, deployed classification models and LLM solutions, and built monitoring pipelines for production systems. Main gaps are limited PyTorch experience and narrow industry exposure, but shows high growth potential and strong technical fundamentals. Good cultural fit for autonomous, technically rigorous environment.
Top Strengths
- ✓Extensive production ML experience with end-to-end pipeline ownership
- ✓Strong MLOps background with Kubeflow, Vertex AI, and model monitoring
- ✓Multi-cloud expertise (AWS, GCP) with infrastructure management skills
- ✓Leadership and mentoring experience in data science teams
- ✓Proven ability to deploy complex ML solutions including LLM fine-tuning
Key Concerns
- !Limited PyTorch experience in a PyTorch-heavy role
- !Narrow industry experience (primarily SAP/enterprise software)
Culture Fit
Growth Potential
High
Salary Estimate
$140,000 - $165,000 based on senior ML experience in Vancouver market
Assessment Reasoning
FIT decision based on strong production ML experience (4+ years), demonstrated MLOps expertise with Kubeflow and Vertex AI, multi-cloud proficiency, and leadership experience. While missing PyTorch and Docker experience, the candidate shows strong fundamentals in production ML systems, model deployment, and infrastructure management. The combination of technical depth, production experience, and leadership skills outweighs the specific technology gaps, which can be addressed through onboarding.
Interview Focus Areas
Experience Overview
11y total · 4y relevantExperienced data scientist with 4+ years of production ML experience at SAP, demonstrating strong MLOps practices, cloud infrastructure skills, and model deployment capabilities. Has led teams and deployed multiple classification models and LLM solutions in production environments.
Matching Skills
Skills to Verify