Lead AI, LLM Engineer
Signature Aviation
full-time
Posted on:
Location Type: Hybrid
Location: Orlando • Florida • United States
Visit company websiteExplore more
Job Level
About the role
- Design, build, and deploy AI applications leveraging hosted or proprietary large language models.
- Lead technical design decisions for enterprise generative AI implementations, ensuring solutions are scalable, secure, and production-ready.
- Develop use cases such as:
- Natural language querying of enterprise data
- Document summarization and information extraction
- Conversational AI, copilots, and knowledge assistants
- Decision-support and workflow automation tools
- Develop APIs, microservices, and integrations to expose AI capabilities to internal platforms and applications.
- Ensure solutions are aligned with enterprise architecture, security, and engineering standards.
- Provide technical guidance and design reviews for other engineers contributing to AI-enabled applications.
- Design, test, and optimize prompts for accuracy, consistency, safety, and business alignment.
- Architect orchestration workflows that combine LLMs with external tools, APIs, structured data, and business logic.
- Implement guardrails to mitigate hallucinations, enforce policy constraints, and reduce prompt injection risks.
- Establish repeatable testing frameworks and evaluation methodologies for prompt and workflow performance.
- Drive best practices for prompt engineering, orchestration design, and model interaction patterns across engineering teams.
- Design and implement RAG architectures leveraging enterprise data sources.
- Build and maintain embedding pipelines, vector databases, and retrieval services.
- Ensure relevance, freshness, access control, and compliance for retrieved content.
- Optimize retrieval strategies and grounding techniques to improve response quality and accuracy.
- Provide architectural guidance on enterprise knowledge systems that support AI-driven applications.
- Deploy AI applications into production environments with robust CI/CD pipelines and infrastructure practices.
- Establish monitoring frameworks for:
- Model output quality and drift
- Latency, availability, and reliability
- Cost, usage, and scaling patterns
- Diagnose and resolve production incidents related to AI systems.
- Improve system robustness through testing, logging, and observability best practices.
- Guide engineering teams on operational standards for running reliable AI services in production environments.
- Implement safeguards for data privacy, security, and access control.
- Ensure AI systems comply with enterprise governance and regulatory standards.
- Support explainability, auditability, and responsible AI practices.
- Prevent leakage of sensitive or proprietary information in AI interactions.
- Partner with security, legal, and governance teams to establish guardrails for enterprise AI adoption.
- Partner closely with:
- Director of Data Science & AI
- Data Scientists and ML Engineers
- Data Engineers and Platform Engineers
- Legal, Security, and Governance teams
- Lead technical design discussions and architecture reviews for AI solutions.
- Participate in Agile delivery processes and guide implementation planning for complex AI initiatives.
- Communicate risks, trade-offs, assumptions, and limitations of AI solutions to both technical and non-technical stakeholders.
- Mentor engineers and contribute to the growth of AI engineering capabilities within the organization.
Requirements
- Typically requires a minimum of 8 years of related experience with a bachelor’s degree in Computer Science, Engineering, Statistics, or a related quantitative field (or equivalent experience).
- 6–10+ years of experience in software engineering, ML engineering, or AI engineering.
- Demonstrated experience building and deploying LLM-based or generative AI applications in production environments.
- Experience designing scalable AI systems or platforms supporting enterprise use cases.
- Additional knowledge and skills:
- Strong proficiency in Python and/or JavaScript
- Experience with LLM platforms and APIs
- Experience implementing RAG architectures and vector databases
- Familiarity with modern data platforms (e.g., Databricks, Snowflake)
- Experience with cloud platforms (AWS, Azure, or GCP)
- Understanding of MLOps and CI/CD concepts
- Experience fine-tuning or training LLMs
- Familiarity with AI safety, prompt injection risks, and mitigation techniques
- Experience building internal AI platforms or copilots
- Strong problem-solving and systems thinking skills
- Ability to explain AI behavior and limitations to non-technical stakeholders
- Clear written and verbal communication skills
- Pragmatic, impact-oriented mindset
Benefits
- Medical/prescription drug, dental, and vision Insurance
- Health Savings Account
- Flexible Spending Accounts
- Life Insurance
- Disability Insurance
- 401(k)
- Critical Illness, Hospital Indemnity and Accident Insurance
- Identity Theft and Legal Services
- Paid time off
- Paid Maternity Leave
- Tuition reimbursement
- Training and Development
- Employee Assistance Program (EAP) & Perks
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
PythonJavaScriptLLM-based applicationsgenerative AI applicationsRAG architecturesvector databasesMLOpsCI/CDprompt engineeringworkflow automation
Soft Skills
problem-solvingsystems thinkingcommunicationmentoringleadershipcollaborationtechnical guidancedesign reviewsimpact-oriented mindsetexplainability