Principal AI Solutions Engineer
New
B
Bausch + LombEye health
United StatesFull-TimePrincipal
Salary165,000 - 190,000 USD per year
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Job Details
- Experience
- 5+ years of experience in software engineering, automation, process engineering, or related technical roles; 1+ year hands-on experience building with LLM APIs and/or agent frameworks
- Required Skills
- PythonRESTful APIs
Requirements
- Hold a bachelor’s degree in business, Engineering, Computer Science, Information Systems, or a related field; an advanced degree is preferred.
- Have 5+ years of experience in software engineering, automation, process engineering, or related technical roles delivering production solutions.
- Have 1+ year of hands-on experience building with LLM APIs and/or agent frameworks; production shipping experience is preferred.
- Have experience in consulting-style or advisory roles, including problem structuring, executive communication, and influencing without authority.
- Demonstrate the ability to translate ambiguous workflows into scalable enterprise solutions.
- Communicate effectively across business needs and technical implementation.
- Preferred: Experience deploying solutions with enterprise AI platforms and services, such as Azure AI Foundry.
- Preferred: Familiarity with a model garden approach and selecting models based on task quality, latency, and cost.
- Preferred: Experience in regulated, complex, or global enterprise environments and working within security or compliance constraints.
- Preferred: Background in process transformation, operational excellence, or management-consulting-style problem solving.
Responsibilities
- Diagnose end-to-end processes and identify high-value workflows for AI-driven transformation.
- Map workflows, SOPs, controls, and decision logic into agent-friendly steps.
- Facilitate working sessions with business SMEs to define reasoning, execution, validation, escalation, and human-in-the-loop steps.
- Partner with business leaders to frame problems, quantify impact, and prioritize AI solution hypotheses.
- Build AI agents using Python and modern agent frameworks to analyze information, follow procedures, call tools and APIs, and complete tasks.
- Build logic layers and integrations across APIs, workflow tools, data platforms, and approved enterprise data sources.
- Deploy solutions using enterprise AI platforms such as Azure AI Foundry, with security, access controls, monitoring, and reliability practices.
- Implement guardrails, observability, evaluation, and fallback mechanisms for safe and reliable operation.
- Manage work through backlogs and iterative releases in collaboration with IT, data, security, and platform teams.
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