United States onlyAbout the role
We're looking for an AI Engineer who is equal parts builder and thinker — someone who gets energized by turning complex ideas into production-ready AI systems that actually work at scale. This role sits at the intersection of machine learning, software engineering, and practical business impact.
You'll design, develop, and deploy end-to-end AI solutions — with a strong emphasis on LLM-powered applications, intelligent automation, and GenAI integrations. You won't just experiment in notebooks; you'll ship to production, own the lifecycle, and collaborate closely with cross-functional teams to ensure your work drives measurable outcomes.
What you'll do
- Design and build production-grade AI/ML systems, including LLM-powered pipelines, RAG architectures, and agentic AI workflows
- Develop and maintain scalable MLOps pipelines covering model training, deployment, monitoring, and lifecycle management
- Integrate AI capabilities into enterprise platforms and workflows using tools like LangChain, Autogen, OpenAI API, and Hugging Face
- Collaborate with data scientists, product stakeholders, and software engineers to translate prototypes into reliable, scalable solutions
- Leverage cloud platforms (AWS, Azure, or GCP) to build and scale AI infrastructure and services
- Create clear technical documentation for AI models, systems, and workflows
- Stay current on emerging AI frameworks
- 3–7 years of experience in AI/ML engineering with hands-on deployment experience (not just research or prototyping)
- Strong proficiency in Python and familiarity with frameworks such as TensorFlow, PyTorch, or Keras
- Practical experience with LLMs, prompt engineering, and GenAI application development
- Hands-on experience with at least one major cloud platform (AWS, Azure, or GCP) and containerization tools (Docker, Kubernetes)
- Solid understanding of MLOps principles, CI/CD workflows, and model governance
- Strong communication skills — you can explain AI concepts clearly to both technical and non-technical audiences
- Bachelor's or Master's degree in Computer Science, Engineering, Applied Mathematics, or a related field — or equivalent practical experience
- Experience with Agentic AI frameworks (LangGraph, Autogen, CrewAI)
- Contributions to open-source AI projects or published research
- Familiarity with infrastructure-as-code tools (Terraform, Bicep)
- Experience deploying AI in regulated industries such as healthcare, finance, or legal
Why this role
- Fully remote — no commute, no office mandates, full flexibility to do your best work from home
- Work on meaningful, real-world AI applications with direct business impact — not just internal tools
- Competitive compensation benchmarked to Atlanta market rates with room to grow
- Collaborative, low-bureaucracy environment where engineers have a voice in technical direction
- Access to the latest AI tooling and encouragement to experiment and innovate
Our client is an EEOC Employer and encourages all minority groups to apply.
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