Agentic AI developer

Purple Drive Technologies

$90K — $130K *
Information Technology
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • 5+ years of experience in AI development with a focus on agentic systems.
  • Proficiency in Python, particularly for AI applications.
  • Hands-on knowledge of LangChain and LangGraph frameworks.
  • Experience with Azure AI Foundry for deploying AI solutions.
  • Familiarity with regulatory considerations in AI systems.

Responsibilities

  • Design and optimize AI agents using multi-agent frameworks.
  • Implement advanced prompting strategies to improve decision-making.
  • Integrate AI agents with existing APIs and databases for automated actions.
  • Create memory systems to maintain context during processes.
  • Move AI prototypes into production while ensuring performance metrics.
  • Establish safety and governance protocols for ethical AI operation.

Benefits

  • Opportunity to work with cutting-edge AI technologies.
  • Collaborative work environment with innovative teams.
  • Professional development and advancement opportunities.
  • Flexible work hours and remote working options available.
  • Focus on ethical AI development and regulatory compliance.
Full Job Description
Overview:

Role: Agentic AI developer

Location: Pittsburgh, PA

Job Description

Skill: Agentic AI Developer

Must Have Technical/Functional Skills:

  • 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:

  • Python, LangChain, LangGraph, Azure AI Foundry.


Roles & Responsibilities:

  • 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.

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