Senior Agentic AI Engineer - Hybrid (3 Onsite Days)

Global Technical Talent, an Inc. 5000 Company

Glendale (AZ)

Hybrid

USD 147,282,000 - 191,152,000

Full time

2 days ago

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Benefits offered by this job

Hybrid schedule

Job summary

Global Technical Talent seeks a hands-on Senior Agentic AI Engineer to design, build, and operate production-grade AI systems on Microsoft Azure for the Digital Supply Chain Systems organization in Glendale, AZ. You will develop reusable, scalable, and explainable enterprise AI capabilities supporting evolving business requirements.

The role emphasizes architecture, RAG data pipelines, NL2SQL, and guardrails for Responsible AI.

Qualifications

8 years of relevant software engineering experience.
3 years of hands-on experience developing Generative AI, machine learning, intelligent automation, or LLM-based applications, including substantial recent experience with Agentic AI.
Bachelor's degree in Computer Science or a related field; Master’s preferred.

Responsibilities

Design and build production-grade single-agent and multi-agent systems in Azure AI Foundry.
Architect scalable Generative AI solutions leveraging Azure services.
Build and govern RAG architectures using enterprise data sources.
Develop secure, reliable integrations between agents and enterprise systems.
Design NL2SQL capabilities with secure data access.
Define semantic-layer strategies for enterprise data models and terminology.
Architect decision intelligence to support informed decision-making.
Establish reusable frameworks for impact analysis and decision traceability.
Ensure explainable, grounded AI recommendations with authoritative data.
Define evaluation methodologies for response and reasoning quality.
Implement guardrails and Responsible AI controls; ensure compliance and safety.
Establish governance, security, safety, transparency, and compliance standards.
Implement observability and cost-optimized AI telemetry and dashboards.
Lead architectural decisions for cloud-native AI platforms; mentor engineers.
Collaborate with stakeholders to translate requirements into reusable AI capabilities.
Partner with data engineering for AI-ready data products and metadata frameworks.
Stay current with emerging AI technologies and industry practices.