Madison-Davis, LLC

Description

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.

Why This Role

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 at scale. You'll join a high-performing engineering group that's delivering enterprise-grade AI capabilities across multiple business lines.

What You'll Do

• 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.

What You Bring

• 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 DevOps practices.

Nice to Have (Highlight These on Your Resume)

• 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 Sagemaker, or Google Vertex AI.
• Background in AI/ML lifecycle management - MLflow, Databricks, or Dataiku.
• Understanding of MLOps or LLMOps principles.
• Exposure to TensorFlow or PyTorch.
• Experience integrating AI models into enterprise or regulated environments.
• Familiarity with containerized cloud environments (Docker, Kubernetes).
• Version control experience with GitHub 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.

What's In It for You

• 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.