Back
Strong employer brand and metro location increase applicant density despite niche senior SRE+AI specialization.
Requires combined SRE and AI architecture expertise, transferable across industries but still domain-specialized.
Explicit 12+ years, advanced degree and specialized AI/SRE skills make filters highly stringent.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Define and implement architectural patterns for ML models, LLMs, AI agents, and computer vision to embed Site Reliability Engineering (SRE) and quality assurance principles within application architecture.
Collaborate with Data and AI Architecture teams and Enterprise Architecture to align AI/ML solutions with enterprise standards and cloud-native best practices across Azure, AWS, and GCP.
Lead AI observability, scalability, reliability, and SRE orchestration platform design using AI-driven automation, proactively preventing major incidents and improving ecosystem performance.
Master's or Ph.D. in Computer Science, AI/ML, or related field.
12+ years experience in AI/ML architecture and enterprise-scale AI solution deployment.
Strong expertise in Azure and AWS AI/ML cloud-native architectures.
Hands-on experience with LLMs, AI agents, MLOps pipelines, AI observability (model monitoring, explainability, drift detection), and programming with Python and AI/ML frameworks (TensorFlow, PyTorch).
Demonstrates deep technical expertise combining SRE principles with AI/ML architecture across multi-cloud environments for highly scalable, reliable AI solutions.
Experienced in collaborating cross-functionally with data scientists, engineers, and IT leadership to integrate AI observability and governance into enterprise applications.
Skilled in driving AI Ops adoption and designing AI-driven orchestration platforms to proactively prevent incidents and optimize operations.