Agentic AI Developer

VOLTO Consulting; Dallas (TX); On-site; USD 120,000 - 190,000; Full time

Job summary

VOLTO Consulting seeks an Agentic AI Developer to design, build, and operationalize governed AI agent systems that autonomously plan, reason, and execute complex workflows across enterprise data and risk platforms.

The role emphasizes developing multi-agent, event-driven AI solutions using Python, LangChain, LangGraph, and Azure AI Foundry, with a strong focus on explainability, human-in-the-loop control, and regulatory readiness.

Qualifications

• Design, build, and operationalize governed AI agent systems for enterprise workflows.
• Develop multi-agent, event-driven AI solutions with Python, LangChain, LangGraph, and Azure AI Foundry.
• Prioritize explainability, human-in-the-loop control, and regulatory readiness.

Responsibilities

• Architect and optimize AI agents using multi-agent frameworks.
• Integrate agents with APIs, databases, and SaaS platforms to enable autonomous action-taking.
• Implement AI guardrails, human-in-the-loop approval steps, and audit trails.

Skills

Agentic System Design

Reasoning & Planning

Tool Integration

Memory & Context

Production Deployment

Safety & Governance

Tools

LangGraph

AutoGen

CrewAI

Job description

• The Agentic AI Developer will design, build, and operationalize governed AI agent systems that autonomously plan, reason, and execute complex workflows across enterprise data and risk platforms.
• This role focuses on developing multi agent, event driven AI solutions using Python, LangChain, LangGraph, and Azure AI Foundry, with strong emphasis on explainability, human in the loop control, and regulatory readiness.

Primary Skills:

• Agentic System Design: Architect, build, and optimize AI agents using multi-agent frameworks (e.g., LangGraph, AutoGen, CrewAI).
• Reasoning & Planning: Implement advanced prompting strategies, such as chain-of-thought, reflection, and self-correction loops to enhance agent decision-making.
• Tool Integration: Connect AI agents to APIs, databases, and third-party SaaS platforms to enable autonomous action-taking.
• Memory & Context: Design short-term and long-term memory systems (RAG, vector databases) so agents can maintain state and context over long-running workflows.
• Production Deployment: Transition prototypes from research to production-ready systems, ensuring low latency, high accuracy, and observability (e.g., using Amazon CloudWatch, LangSmith).
• Safety & Governance: Implement AI guardrails, human-in-the-loop approval steps, and audit trails to ensure ethical and secure operation.