Architect and implement end‑to‑end pipelines for data ingestion, model training, validation, and serving
Develop self‑service APIs and CLI tools for data submission and resource management
Build internal SDKs, CLI tools, and libraries for model deployment, monitoring, and lifecycle management
Research emerging embedding models and vector-search technologies
Conduct cost‑benefit analyses of alternative cloud providers and managed AI services
Implement AI use cases ensuring compliance with regulations and security standards
Collaborate with data scientists and software engineers to contribute to the roadmap for AI systems

Requirements

Bachelor’s degree in Computer Science, Software Engineering, Information Systems or a related field
8 years relevant experience with a Bachelor's degree, or 6 years with a Master's, or 4 years with a PhD
3 years experience focused on AI or MLOps
2 years working with cloud (AWS or Azure) infrastructure
Experience with relevant programming languages (e.g., Python, Java, Powershell/Bash)
Basic knowledge of database technologies (e.g., SQL, NoSQL) and distributed computing systems
Experience with evaluating and implementing cybersecurity for AI systems
Demonstratable knowledge of matching hardware configuration to AI system requirements
Exceptional verbal and written communication skills

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

data ingestionmodel trainingmodel validationmodel servingself-service APIsCLI toolsSDKsembedding modelsvector-search technologiesMLOps

Soft Skills

communication