Lead Engineer (Generative AI)

Amtex Enterprises Inc; Chicago, San Francisco, Charlotte, Minneapolis (IL, CA, NC, MN); On-site; USD 150,000 - 190,000; Full time

Job summary

Amtex Enterprises Inc. is seeking a Lead Engineer (Generative AI) to design, develop, and operationalize enterprise-scale GenAI solutions.

The role combines deep hands-on work with technical leadership across LLMs, RAG, and agentic AI architectures. You will partner with product and business teams to translate requirements into production-grade AI capabilities aligned with security, risk, and governance standards.

Qualifications

• Bachelor's degree or equivalent; 6–8 years of relevant experience.
• 8+ years of software engineering, platform engineering, or AI/ML solutions.
• 2+ years hands-on GenAI experience with LLMs and RAG architectures.

Responsibilities

• Design, develop, and deploy GenAI solutions at enterprise scale.
• Lead GenAIOps, lifecycle management, and secure deployment.
• Architect scalable cloud-genAI applications across AWS/Azure.

Skills

GenAI architectures

LLMs

RAG architectures

Python

Distributed systems

Mentoring

LangChain

LangGraph

Education

Bachelor's degree in CS or related

MS preferred

Tools

AWS Bedrock

Terraform

Kubernetes

Docker

Azure

Foundry Agent Service

Job description

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 Description

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

1. GenAI Solution Engineering
   • 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 LangChain, LangGraph, AWS Bedrock, and Microsoft Foundry Agent Service
   • Evaluate emerging tools and frameworks to continuously improve solution quality and innovation
2. GenAIOps & Lifecycle Management
   • 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
3. Cloud, Platform & Scalability Engineering
   • 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
4. Software Engineering & Architecture
   • 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
5. Technical Leadership & Influence
   • 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

Basic Qualifications

• Bachelor’s degree, or equivalent work experience
• Six to eight years of relevant experience

Experience Should Include

• 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., LangChain, LangGraph)
• 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

Preferred Qualifications

• 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

Core Competencies

Technical Depth & Innovation

• Strong expertise in GenAI architectures and evolving AI technologies
• Ability to balance experimentation with enterprise-grade reliability

Architecture & Systems Thinking

• Designs scalable, distributed, and resilient systems
• Aligns architecture decisions with enterprise standards and long-term strategy

Execution & Operational Excellence

• Drives end-to-end delivery from concept through production
• Ensures high standards for quality, security, and performance

Leadership & Collaboration

• Influences without authority and leads through technical expertise
• Mentors engineers and elevates overall team capability

Business & Stakeholder Alignment

• Translates complex technical concepts into business outcomes
• Partners effectively across product, engineering, and leadership teams