Job Title: Agentic AI Lead Engineer
Location(s) - Woodland Hills, CA/Atlanta, GA/Richmond, VA
Name | | Required | |
Google Cloud Platform | | Yes | |
Vector Databases | | Yes | |
Large Language Model (LLM) | | Yes | |
TypeScript | | Yes | |
LLMs - Open Sources | | Yes | |
Azure | | Yes | |
AWS Cloud | | Yes | |
Python | | Yes | |
AI | | Yes | |
Javascript | | Yes | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
OCR YEs
Top Skills:
Strong programming experience in Python (required); familiarity with JavaScript/TypeScript is a plus
Hands-on experience with LLMs, Generative AI, and agent frameworks (e.g., LangChain, LangGraph, AutoGen, CrewAI or similar)
Experience building workflow automation solutions using APIs, event-driven systems, and microservices
Role Summary:
The Agentic AI Engineer is responsible for designing, building, and operationalizing agentic AI driven automation solutions that autonomously plan, reason, and execute complex enterprise workflows. This role focuses on leveraging multi-agent architectures, LLMs, orchestration frameworks, and enterprise integrations to automate business and IT processes with minimal human intervention, while ensuring security, governance, and Responsible AI compliance.
The engineer will work closely with enterprise architects, domain experts, platform teams, and business stakeholders to transform manual or rule-based workflows into intelligent, adaptive, agent-driven systems that deliver measurable business outcomes
Required Qualifications:
Technical Skills
Knowledge of vector databases, knowledge graphs, and retrieval-augmented generation (RAG) patterns Familiarity with cloud platforms (Azure/AWS/Google Cloud Platform), containers, and infrastructure-as-code Conceptual & Enterprise Skills.
Strong understanding of agentic AI concepts: autonomy, planning, reasoning, action, learning, and reflection.
Experience translating business processes into automated, AI-driven workflows.
Awareness of Responsible AI, security, and governance considerations in enterprise environments.
Preferred Qualifications:
.
Experience implementing multi-agent orchestration in production environments.
Background in enterprise platforms such as ERP, CRM, ServiceNow, or supply chain systems.
Exposure to industry-specific agentic AI use cases (e.g., IT operations, SDLC automation, supply chain, customer service).
Experience collaborating with architects, program leads, and domain experts in large-scale transformations.
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