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AI/ML Software Developer -Need 15+years

Jobs via Dice

Austin · Hybrid Full-time Senior Today

About the role

Job Title

AI/ML Software Developer – RAG

Location

Austin, TX (Hybrid)

Work Type

Contract (C2C)

Experience Required

15+ Years (Senior Level Only)

Interview Mode

In-Person Only (No Exceptions)

Work Schedule

  • Hybrid Role
  • 3 Days Remote
  • 2 Days Onsite (Monday & Thursday Mandatory)

Important Notes

  • Only LOCAL candidates (Austin Metro Area)
  • Out-of-state candidates NOT allowed
  • Candidates must already be residing in Texas
  • No relocation candidates

Job Summary

We are seeking a highly experienced AI/ML Software Developer specializing in Retrieval-Augmented Generation (RAG) and agentic AI systems. The candidate will design, develop, and deploy AI-driven autonomous solutions to improve productivity, automate workflows, and support intelligent decision-making with a strong focus on governance, security, and cost efficiency.

Key Responsibilities

  • Design and develop AI-driven agentic workflows and autonomous systems
  • Build and implement RAG architectures using vector databases
  • Develop, test, and deploy scalable AI/ML solutions
  • Collaborate with developers, UX designers, and business analysts
  • Implement AI governance, safety controls, and content filtering
  • Optimize LLM performance, token usage, and cost efficiency
  • Ensure secure data handling (PII/PHI compliance)
  • Integrate LLMs via APIs and enterprise systems

Required Skills

  • Strong experience in AI/ML engineering or advanced data science
  • Proven experience building production‑grade autonomous agents
  • Expertise in context engineering
  • Hands‑on experience with:
    • LangChain
    • LangGraph
    • CrewAI
    • AutoGPT
  • Experience with RAG architectures & vector databases
  • Strong Python programming skills
  • Experience with AI/ML libraries:
    • OpenAI
    • Hugging Face
    • Azure AI
  • Experience integrating LLMs via APIs
  • Knowledge of AI governance and model lifecycle management
  • Experience implementing Model Context Protocol (MCP)
  • Experience with AI guardrails and safety mechanisms
  • Understanding of data privacy (PII/PHI)
  • Experience in multi‑agent systems
  • LLM optimization (cost, tokens, performance)
  • Enterprise AI deployment & scalability knowledge

Nice to Have

  • Experience in large‑scale enterprise AI implementations
  • Strong understanding of secure AI system design

Requirements

  • Strong experience in AI/ML engineering or advanced data science
  • Proven experience building production-grade autonomous agents
  • Expertise in context engineering
  • Hands-on experience with LangChain
  • Hands-on experience with LangGraph
  • Hands-on experience with CrewAI
  • Hands-on experience with AutoGPT
  • Experience with RAG architectures & vector databases
  • Strong Python programming skills
  • Experience with AI/ML libraries: OpenAI
  • Experience with AI/ML libraries: Hugging Face
  • Experience with AI/ML libraries: Azure AI
  • Experience integrating LLMs via APIs
  • Knowledge of AI governance and model lifecycle management
  • Experience implementing Model Context Protocol (MCP)
  • Experience with AI guardrails and safety mechanisms
  • Understanding of data privacy (PII/PHI)
  • Experience in multi-agent systems
  • LLM optimization (cost, tokens, performance)
  • Enterprise AI deployment & scalability knowledge

Responsibilities

  • Design and develop AI-driven agentic workflows and autonomous systems
  • Build and implement RAG architectures using vector databases
  • Develop, test, and deploy scalable AI/ML solutions
  • Collaborate with developers, UX designers, and business analysts
  • Implement AI governance, safety controls, and content filtering
  • Optimize LLM performance, token usage, and cost efficiency
  • Ensure secure data handling (PII/PHI compliance)
  • Integrate LLMs via APIs and enterprise systems

Skills

AIAI governanceAI guardrailsAI/MLAutoGPTAzure AICrewAIHugging FaceLangChainLangGraphLLMMLModel Context Protocol (MCP)OpenAIPythonRAGVector databases

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