Lead AI Engineer (Agentic AI) - NC, TX
$69 - $74 per hourApex Systems
Software Engineer 4 / Lead AI Engineer (Agentic AI)
Client: Financial Services
Location: Raleigh, NC / Charlotte, NC (CIC) / Irving, TX – Hybrid (3 days onsite mandatory)
Contract Length: 12 months (possible extension or conversion)
Pay Rate: $69 - $74
Top Requirements:
- 5–10 years of Software Engineering experience with strong Python development (2+ years)
- Hands-on experience with Generative AI frameworks (LangChain, ADK, agent-based architectures)
- Strong understanding of SDLC and ability to design, build, and deploy scalable applications
Plusses:
- Experience with MLOps / LLMOps and deploying AI models in production
- Experience with multi-cloud platforms (Azure, GCP, OpenShift)
- Background in DevOps (CI/CD, infrastructure as code)
- Financial services or regulated industry experience
- Cybersecurity awareness and safe AI development practices
Job Summary:
In this contingent resource assignment, you may: Consult on complex initiatives with broad impact and large-scale planning for Software Engineering. Review and analyze complex multi-faceted, larger scale or longer-term Software Engineering challenges that require in-depth evaluation of multiple factors including intangibles or unprecedented factors. Contribute to the resolution of complex and multi-faceted situations requiring solid understanding of the function, policies, procedures, and compliance requirements that meet deliverables. Strategically collaborate and consult with client personnel.
Day-to-Day Responsibilities:
- Design and develop generative AI applications leveraging LLMs and enterprise data
- Build agent-based AI solutions using frameworks such as LangChain and Agent Development Kit (ADK)
- Develop Python-based services and applications supporting AI initiatives
- Implement retrieval-augmented generation (RAG) pipelines and intelligent workflows
- Collaborate with architects and engineers to align with enterprise technology strategies
- Support cloud-native development and application deployment across Azure, GCP, and OpenShift
- Contribute to MLOps/LLMOps practices including model deployment, monitoring, and scaling
- Identify and resolve technical issues across development, build, and deployment pipelines
- Participate in peer code reviews and enforce engineering best practices
- Build prototypes and demos of AI solutions for internal business stakeholders
- Work in Agile development environments with version control and branching strategies
- Ensure security, scalability, and compliance of AI-enabled applications
- Influence technical roadmaps and contribute to innovation initiatives across the organization
Vacancy posted 3 days ago
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