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.