Lead Engineer (Generative AI
Job in
Charlotte, Mecklenburg County, North Carolina, 28245, USA
Listed on 2026-08-20
Listing for:
Amtex Enterprises Inc
Full Time
position Listed on 2026-08-20
Job specializations:
-
Software Development
AI Engineer (Applied/Software), Software Architect, Cloud Engineer - Software, DevOps
Job Description & How to Apply Below
Job Title :
Lead Engineer (Generative AI)
Duration: 6-12 plus months
Location- Minneapolis/St. Paul “Twin Cities,” MN
- Bay Area, CA – San Francisco and surrounding areas
- Charlotte, NC
- Chicago, IL
Job Summary
The Lead Engineer (Generative AI) is a senior technical role responsible for designing, developing, and operationalizing enterprise-scale Generative AI (GenAI) solutions. This position combines deep hands-on expertise in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and agentic AI architectures with strong technical leadership to deliver secure, scalable, and resilient AI systems.
The role partners across engineering, product, and business teams to translate complex requirements into production-ready AI capabilities aligned with enterprise standards for security, risk, and responsible AI.
Key Responsibilities- Design, develop, and deploy GenAI solutions leveraging:
- Large Language Models (LLMs)
- Retrieval-Augmented Generation (RAG) architectures
- Prompt engineering techniques
- Agentic AI workflows and orchestration
- Build intelligent systems using frameworks such as Lang Chain, Lang Graph, AWS Bedrock, and Microsoft Foundry Agent Service
- Evaluate emerging tools and frameworks to continuously improve solution quality and innovation
- Lead the end-to-end lifecycle of GenAI solutions, including:
- Solution architecture and engineering
- Integration with enterprise systems
- Secure deployment and release management
- Monitoring, observability, and continuous optimization
- Implement GenAIOps best practices to ensure scalability, reliability, and cost efficiency
- Establish logging, evaluation, and feedback mechanisms for production AI systems
- Architect and deploy GenAI applications across cloud environments (Azure and AWS)
- Design distributed systems capable of supporting high-throughput, low-latency AI workloads
- Leverage modern infrastructure practices:
- Containerization (Docker)
- Orchestration (Kubernetes)
- Infrastructure as Code (Terraform, ARM/Bicep)
- Ensure high availability, performance, and enterprise-grade security
- Develop scalable, maintainable applications using Python and microservices-based architectures
- Apply secure coding standards and robust data handling practices for regulated environments
- Build and manage CI/CD pipelines supporting automated testing, deployment, and release management
- Enforce engineering best practices including code reviews, testing, and documentation
- Provide architectural leadership and guidance across GenAI initiatives
- Drive critical design decisions for large-scale, complex AI solutions
- Mentor and coach senior engineers and development teams
- Translate business requirements into scalable, secure, and resilient technical solutions
- Partner with stakeholders across product, business, risk, and security functions
- Bachelor’s degree, or equivalent work experience
- Six to eight years of relevant experience
- Bachelor’s or Master’s degree in Computer Science, Engineering, or related field
- 8+ years of experience in software engineering, platform engineering, or AI/ML solutions
- 2+ years hands-on experience with GenAI technologies, including LLMs and RAG architectures and vector databases
- Strong knowledge of agentic AI concepts and frameworks (e.g., Lang Chain, Lang Graph)
- Experience with cloud platforms (Azure and/or AWS)
- Deep understanding of distributed systems and scalable architecture patterns
- Proficiency in Python and microservices-based development
- Experience with Docker, Kubernetes, and Infrastructure as Code tools
- Demonstrated technical leadership and mentoring experience
- Experience implementing GenAI solutions in enterprise or regulated environments
- Familiarity with observability frameworks and AI lifecycle tooling
- Understanding of AI governance, security, and compliance requirements
- Experience contributing to or working with AI/ML or GenAI frameworks
- Background in financial services or other highly regulated industries
- Strong expertise in GenAI architectures and evolving AI technologies
- Ability to balance experimentation with enterprise-grade reliability
- Designs scalable, distributed, and resilient systems
- Aligns architecture decisions with enterprise standards and long-term strategy
- Drives end-to-end delivery from concept through production
- Ensures high standards for quality, security, and performance
- Influences without authority and leads through technical expertise
- Mentors engineers and elevates overall team capability
- Translates complex technical concepts into business outcomes
- Partners effectively across product, engineering, and leadership teams
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