AI Full Stack Engineering Lead
On-siteCharlotte, North Carolina, United States
Job Summary
Lead end-to-end delivery of AI and non-AI software solutions across multiple business domains, designing scalable architectures using microservices, event-driven patterns, and cloud-native principles. Implement large language models, RAG pipelines, and agent-based systems while integrating capabilities via APIs and microservices. Own delivery accountability from planning through production rollout, ensuring adherence to enterprise security, compliance, and performance standards. Provide technical leadership by mentoring engineers, conducting design reviews, and translating business requirements into high-quality technical implementations. Manage CI/CD pipelines, system observability, and incident resolution to drive production readiness.
Required Qualifications
- 10+ years of experience in software engineering and system design
- Proven experience delivering large-scale enterprise applications and AI solutions
- Strong expertise in: Java (Spring Boot, Microservices), Python (AI/ML, APIs, data engineering)
- Hands-on experience with: AI/ML frameworks (OpenAI, Hugging Face, LangChain,etc)
- RAG pipelines, embeddings, vector databases
- RESTful APIs, distributed systems
- Deep understanding of: Microservices, APIs, event-driven architectures
- Cloud platforms (Azure preferred)
- Containerization (Docker, Kubernetes)
- Practical experience with: LLM-based applications and prompt engineering
- Model lifecycle management (training, deployment, monitoring)
- AI governance, risk, and explainability (preferred in regulated industries)
- Strong problem-solving and analytical thinking
- Excellent communication and stakeholder management
- Ability to operate in a fast-paced, ambiguous environment
- 1st shift (United States of America)
- 40 Hours Per Week
Desired Qualifications
- Experience in financial services or regulated industries
- Exposure to Microsoft ecosystem (Copilot Studio, Foundry, Fabric)
- Familiarity with agent orchestration, MCP, and AI platform integration patterns
- Experience with data privacy, compliance, and secure AI deployments
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