Lead Generative AI Engineer

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We’re supporting a major global financial technology organization that’s making significant investments in AI innovation. They’re scaling their engineering teams across North America to drive development of next-generation Generative AI solutions. Multiple openings are available for engineers at varying levels — from early-career developers to senior leads and architects — across areas like AI platform engineering, chatbot development, and data engineering for AI-driven systems.

This is a chance to be part of a global enterprise that’s putting real resources behind AI strategy — building tools, platforms, and models that impact client experiences and internal productivity ’ll join a high-performing engineering group that’s delivering enterprise‑grade AI capabilities across multiple business lines.

• Build and enhance production‑grade AI and LLM‑based systems for enterprise applications.
• Contribute to model fine‑tuning, prompt optimization, and training workflows.
• Develop APIs, microservices, and SDKs for internal and client‑facing AI products.
• Collaborate with engineering and data teams to operationalize AI solutions and support MLOps/LLMOps processes.
• Partner cross‑functionally to design and deliver reliable, scalable AI integrations.

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field (or equivalent practical experience).
• 4+ years of hands‑on Python development experience.
• Strong understanding of Generative AI, LLMs, and related model architectures.
• Experience working with NLP, model training, and fine‑tuning workflows.
• Solid grasp of Linux environments and modern Dev Ops practices.

• Hands‑on experience with frameworks like Flask, Django, or FastAPI.
• Familiarity with Python libraries such as numpy, pandas, scikit‑learn, matplotlib, or opencv.
• Experience deploying AI solutions using cloud services like Azure OpenAI, AWS Bedrock, AWS Sage Maker, or Google Vertex AI.
• Background in AI/ML lifecycle management — MLflow, Databricks, or Dataiku.
  
  Understanding of MLOps or LLMOps principles.
• Exposure to Tensor Flow or PyTorch.
• Experience integrating AI models into enterprise or regulated environments.
• Familiarity with containerized cloud environments (Docker, Kubernetes).
• Version control experience with Git Hub or Bitbucket.
• Bonus: experience working with conversational AI platforms (e.g., Copilot Studio, Kore.ai, Amelia).
• Experience collaborating with software development teams to embed AI into core applications.

• Join an organization that’s putting real investment behind AI and automation initiatives.
• Work on cutting‑edge technology in a large‑scale, data‑rich environment.
• Collaborate with top‑tier engineers and data scientists driving AI innovation in financial technology.
• Opportunities for career growth across multiple teams and projects.

Sandy Springs, GA $–$ 6 days ago