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Generative Ai Engineer Agentic Systems

  • Generative Ai Engineer Agentic Systems job type Posted on: Oct 19, 2026
  • Generative Ai Engineer Agentic Systems experience level ConglomerateIT LLC
  • Generative Ai Engineer Agentic Systems employment type Dallas, Texas
  • Generative Ai Engineer Agentic Systems employment type Onsite
  • Generative Ai Engineer Agentic Systems salary Full-time

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Job Title :

Generative Ai Engineer Agentic Systems

Job Type :

Full-Time

Job Location :

Dallas Texas United States

Remote :

No

Jobcon Logo Job Description :

Job Title: Generative AI Engineer Agentic Systems
Location: Dallas, TX (Onsite, 4 5 days a week)
Type: Long-term contract
Interview Process: 2 in-person rounds

About the Role
We're looking for a seasoned engineer to build and launch enterprise-level AI solutions powered by Generative AI and autonomous agents. You'll work hands-on with AWS Bedrock, AgentCore, LangGraph, LangChain, MCP, and RAG to bring smart, production-ready applications to life.

What You'll Do

Agent Development

  • Architect and build single-agent and multi-agent systems on AWS AgentCore.
  • Create and fine-tune agent workflows with LangGraph and LangChain.
  • Give agents the ability to plan, reason, use tools, and retain memory.
  • Define how agents talk to each other and to outside systems.
  • Work with Model Context Protocol (MCP) and tool-calling designs.

RAG & GenAI Solutions

  • Build large-scale RAG pipelines using AWS Bedrock foundation and embedding models.
  • Shape the approach for vectorization, indexing, retrieval, re-ranking, and grounding.
  • Set up prompt engineering frameworks and methods to evaluate GenAI output.
  • Deliver chatbots, knowledge assistants, copilots, and AI-driven automation.
  • Apply Bedrock Guardrails to keep solutions safe, compliant, and responsible.

Apps, APIs & Integration

  • Expose AI features to enterprise apps through RESTful APIs.
  • Connect agents to business apps, workflows, databases, and third-party systems.
  • Build clean, easy-to-use front ends with React and modern UI tools.
  • Create reusable, scalable AI services and microservices.

Cloud & DevOps

  • Deploy and run AI solutions on AWS using Infrastructure as Code practices.
  • Build and maintain infrastructure with Terraform.
  • Set up and manage CI/CD pipelines in GitLab.

Standards & Collaboration

  • Set best practices, standards, and reference designs for GenAI and agentic work.
  • Partner with product owners, architects, data engineers, and business teams.
  • Make sure every solution meets security, governance, compliance, and responsible AI needs.

What You Bring

  • 7+ years in software engineering, with a strong track record in AI/ML.
  • 4+ years hands-on with Generative AI, LLMs, and RAG.
  • 2+ years building agentic or multi-agent systems.
  • Proven experience shipping production AI apps in the cloud.
  • Bachelor's or Master's in Computer Science, Engineering, AI, Data Science, or a related field.

Nice to Have

  • Built enterprise AI copilots or conversational assistants.
  • Advanced orchestration experience with LangGraph.
  • Used frameworks to evaluate, test, and benchmark AI agents.
  • Worked with knowledge graphs and graph-based reasoning.
  • Set up Human-in-the-Loop (HITL) workflows.

Founded in 2014,

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Jobcon Logo Position Details

Posted:

Oct 19, 2026

Reference Number:

1068-25021

Employment:

Full-time

City:

Dallas

Job Origin:

CIEPAL_ORGANIC_FEED

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Job Title: Generative AI Engineer Agentic Systems
Location: Dallas, TX (Onsite, 4 5 days a week)
Type: Long-term contract
Interview Process: 2 in-person rounds

About the Role
We're looking for a seasoned engineer to build and launch enterprise-level AI solutions powered by Generative AI and autonomous agents. You'll work hands-on with AWS Bedrock, AgentCore, LangGraph, LangChain, MCP, and RAG to bring smart, production-ready applications to life.

What You'll Do

Agent Development

  • Architect and build single-agent and multi-agent systems on AWS AgentCore.
  • Create and fine-tune agent workflows with LangGraph and LangChain.
  • Give agents the ability to plan, reason, use tools, and retain memory.
  • Define how agents talk to each other and to outside systems.
  • Work with Model Context Protocol (MCP) and tool-calling designs.

RAG & GenAI Solutions

  • Build large-scale RAG pipelines using AWS Bedrock foundation and embedding models.
  • Shape the approach for vectorization, indexing, retrieval, re-ranking, and grounding.
  • Set up prompt engineering frameworks and methods to evaluate GenAI output.
  • Deliver chatbots, knowledge assistants, copilots, and AI-driven automation.
  • Apply Bedrock Guardrails to keep solutions safe, compliant, and responsible.

Apps, APIs & Integration

  • Expose AI features to enterprise apps through RESTful APIs.
  • Connect agents to business apps, workflows, databases, and third-party systems.
  • Build clean, easy-to-use front ends with React and modern UI tools.
  • Create reusable, scalable AI services and microservices.

Cloud & DevOps

  • Deploy and run AI solutions on AWS using Infrastructure as Code practices.
  • Build and maintain infrastructure with Terraform.
  • Set up and manage CI/CD pipelines in GitLab.

Standards & Collaboration

  • Set best practices, standards, and reference designs for GenAI and agentic work.
  • Partner with product owners, architects, data engineers, and business teams.
  • Make sure every solution meets security, governance, compliance, and responsible AI needs.

What You Bring

  • 7+ years in software engineering, with a strong track record in AI/ML.
  • 4+ years hands-on with Generative AI, LLMs, and RAG.
  • 2+ years building agentic or multi-agent systems.
  • Proven experience shipping production AI apps in the cloud.
  • Bachelor's or Master's in Computer Science, Engineering, AI, Data Science, or a related field.

Nice to Have

  • Built enterprise AI copilots or conversational assistants.
  • Advanced orchestration experience with LangGraph.
  • Used frameworks to evaluate, test, and benchmark AI agents.
  • Worked with knowledge graphs and graph-based reasoning.
  • Set up Human-in-the-Loop (HITL) workflows.

Founded in 2014,

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