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AI/ML Engineer (Agentic AI)

  • Remote (US)
  • Contract
  • Posted November 6, 2026

This is a remote role, open to candidates based in the United States.

About the role

We’re looking for an AI/ML Engineer to design, build and deploy production AI and agentic AI solutions. You’ll work with LangGraph, LangChain, LLMs and NLP on Azure and AWS, and bring the containerization, CI/CD and responsible-AI discipline that production systems need.

What you’ll do

  • Design, build and deploy AI/ML and agentic AI solutions into production.
  • Apply supervised, unsupervised, reinforcement and deep learning, along with NLP, to business problems.
  • Develop LLM-based solutions with LangGraph and LangChain, and libraries such as Hugging Face and the OpenAI API.
  • Test, validate and optimize models for performance and reliability.
  • Build AI/ML workflows on Azure and AWS services such as Databricks, AKS/EKS, Azure Data Factory, Cognitive Services, Logic Apps and Power Automate.
  • Containerize AI/ML applications and orchestrate containers in production.
  • Build and maintain CI/CD pipelines (for example, GitHub Actions).
  • Work with stakeholders to preprocess, analyze and interpret large, complex datasets.
  • Support capacity planning, upgrades and migrations.
  • Build governance, ethics and responsible-AI practices into the development lifecycle.
  • Find and fix code vulnerabilities.

What you’ll bring

  • 8+ years of experience, including 3+ years deploying AI to production.
  • A strong grasp of agentic AI and frameworks such as LangGraph and LangChain.
  • Experience with supervised, unsupervised, reinforcement and deep learning, plus NLP.
  • Experience with Azure and AWS AI/ML services.
  • Proven experience using and deploying LLMs.
  • Experience containerizing AI/ML projects and running them in production.
  • CI/CD experience and strong Python skills.
  • Familiarity with AI governance, ethics and responsible AI.

Nice to have

  • Azure AI Engineer or AWS Machine Learning certifications.
  • Vector databases and RAG architectures.
  • Prompt engineering and LLM fine-tuning.
  • Infrastructure as code: Terraform, Bicep or ARM templates.
  • AI observability tools such as Prometheus, Grafana or Azure Monitor.
  • Work in a regulated industry such as healthcare or finance.
  • Multi-agent orchestration beyond LangGraph and LangChain, such as AutoGen or CrewAI.
  • Experience mentoring junior data scientists or ML engineers.
  • Published research, patents or open-source contributions in AI/ML.