Principal AI Engineer – Automation & AI
New
E
eClinical SolutionsClinical data intelligence
Location: RemoteFull-TimePrincipal
Salary160,000 - 200,000 USD per year
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Job Details
- Experience
- 6–10+ years of software engineering experience with recent hands-on work in AI/LLM systems
- Required Skills
- AWSPythonArtificial IntelligenceGCPAzureLangChain
Requirements
- Bachelor’s degree in Computer Science, Engineering, Data Science, or a related technical field
- 6–10+ years of software engineering experience with recent hands-on work in AI/LLM systems
- Proven experience building and deploying: LLM-powered applications, Agentic workflows, Automation solutions in production environments
- Strong hands-on experience with tools such as: Codex, Claude Code, Gemini, NotebookLM (or similar AI-assisted development tools), LangChain, LangGraph, LlamaIndex, or equivalent frameworks
- Proficiency in: Python, API development and system integration, Cloud platforms (AWS, Azure, or GCP)
- Experience with: RAG architectures and vector databases
- AI evaluation frameworks, guardrails, and monitoring
- Workflow automation tools and enterprise integrations
- Experience working in regulated environments (e.g., life sciences, healthcare, financial services)
- Familiarity with clinical data, CDISC standards, or clinical development workflows
- Experience in high-growth or PE-backed SaaS environments
Responsibilities
- Design and deploy agentic AI workflows and automation solutions across enterprise functions including R&D, Engineering, Professional Services, IT, Finance, Sales, and Marketing
- Build production-grade systems such as: Multi-agent workflows, RAG-based applications, Document intelligence and summarization pipelines, Workflow and process automation solutions
- Rapidly prototype, validate, and deploy solutions in weeks, not months
- Translate business problems into working AI systems
- Design lightweight architectures and iterate quickly
- Develop and implement: Prompt engineering strategies, Orchestration logic, API integrations and data pipelines, Testing, validation, and monitoring frameworks
- Deliver continuous output with weekly or bi-weekly releases
- Build and maintain reusable assets including: Prompt templates, Agent design patterns, Integration utilities
- Contribute to lightweight standards for: Security and data handling, Responsible AI practices, Evaluation and performance monitoring
- Provide technical guidance to a small team of AI engineers and automation specialists
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