Generative AI, Agentic systems, and production-grade AI platforms . This role is
not a pure management role
— the ideal candidate will actively design, build, and scale
AI systems (RAG, agents, evaluation frameworks)
while leading engineering initiatives and influencing platform strategy. The candidate must demonstrate
strong AI + AWS cloud expertise , with proven experience delivering
enterprise-grade AI solutions in production environments .
AI Engineering Leader
Locations: Austin, TX | Charlotte, NC | New York, NY | Tempe, AZ | San Diego, CA, (hands-on AI)
Long-term Contract
Core Responsibilities
AI System Design & Development
Design and build
production-grade GenAI systems , including:
Multi-agent architectures
Retrieval-Augmented Generation (RAG) pipelines
GraphRAG implementations
Autonomous agent workflows and orchestration
Develop and integrate
AI agents with tools, APIs, and enterprise systems
Implement
MCP-based agent communication and tool-use frameworks
Apply advanced
prompt engineering techniques
for reliability and performance
Agentic AI & Evaluation
Build and deploy
multi-agent orchestration systems
Develop and implement:
Agent evaluation frameworks
RAG evaluation pipelines
Measure and optimize:
Output quality
Hallucination rates
Relevance and groundedness
Continuously improve models through
evaluation-driven iteration
Engineering & Platform Development
Develop APIs and services using:
Python (primary)
.NET (preferred)
Build scalable AI services with:
REST APIs
Microservices architecture
Contribute to
web-based AI applications
using:
Angular / TypeScript (preferred)
Integrate AI systems into enterprise workflows and applications
Cloud & Infrastructure (AWS Focus)
Design and deploy AI solutions on
AWS , leveraging:
Lambda, S3, EC2, EKS, Glue, SNS, SQS
Kafka-based streaming architectures
Build scalable and secure AI pipelines using
cloud-native patterns
Implement
cost-efficient and high-performance AI workloads
DevOps & CI/CD
Design and implement
CI/CD pipelines using GitHub Actions
Integrate AI workflows into CI/CD pipelines with strong AWS integration
Ensure:
Automated deployment
Testing and validation of AI systems
Continuous monitoring and iteration
AI Development Tooling
Leverage modern AI development tools and ecosystems, including:
Claude (Claude API / Claude Code)
Cursor AI (AI-assisted development workflows)
Build and optimize
developer workflows using AI-assisted coding tools
Required Qualifications
10+ years of overall engineering experience
5+ years of
hands-on AI/ML / GenAI development in production environments
Strong experience building:
AI agents (minimum 2+ implementations)
GraphRAG systems (minimum 2+ implementations)
MCP-based integrations (minimum 1+)
Proven expertise in:
Multi-agent orchestration
RAG pipelines
Agent and RAG evaluation frameworks
Strong programming skills in:
Python (must-have)
Experience with:
API development and system integration
Strong experience with:
AWS cloud platform (must-have)
Preferred Qualifications
Experience with:
.NET / C# development
Terraform (Infrastructure as Code)
Experience building:
Web applications using Angular / TypeScript
Familiarity with:
Kafka-based streaming systems
Exposure to: