AI Engineer

In this role you will act as a force multiplier for our quantitative analytics teams. Your primary mission will be to design build and deploy Generative AI tools and Large Language Model (LLM) applications that assist our analytical modelers in their day-to-day work. Whether it is building AI co-pilots for code generation creating automated model-documentation generators you will build the AI infrastructure that makes our FC risk team faster more efficient and more innovative.

• Internal AI Tool Development:Architect and build LLM-powered applications AI agents and workflow automations specifically designed to assist model validators and quantitative modelers (e.g. Independent validation agents code-generation agents and Monitoring agents).
• AI-Ops & Infrastructure (LLMOps):Establish robust AI-Ops pipelines to manage the end-to-end lifecycle of Generative AI tools. Implement automated deployment version control prompt tracking and continuous monitoring for model performance and hallucination mitigation.
• Cloud & CI/CD Engineering:Deploy highly available AI microservices using Google Cloud Platform (GCP). Manage CI/CD pipelines using Infrastructure as Code (e.g. Terraform Cloud Build) to ensure seamless and secure continuous integration.
• User-Centric Collaboration:Work closely with analytical modelers to deeply understand their daily bottlenecks data prep challenges and coding workflows translating those pain points into effective AI solutions.
• Security & Governance:Ensure all internal AI tools adhere to Fords strict data privacy security and compliance standards implementing guardrails for internal data usage.

Skills & Knowledge Required

• LLM & GenAI Technologies:Strong hands-on experience with LLM orchestration frameworks (e.g. LangChain Google ADK MCP) and utilizing commercial or open-source foundation models.
• AI-Ops / LLMOps:Proficiency in tools for monitoring tracing and evaluating LLM outputs.
• Vector Databases:Experience working with vector search technologies (e.g. GCP Vertex Vector Search Sentence Transformer ColBERT) for retrieval using encoder based models.
• Software Engineering:4 years of advanced hands-on experience with Python programming and building robust APIs (FastAPI Flask) to serve AI models to end-users.
• Cloud Platform:Extensive experience in Google Cloud Platform (GCP) specifically with Cloud Build Cloud Run GCS BigQuery.
• Analytics Workflow Understanding:Familiarity with the general workflows of data scientists and modelers (data wrangling feature engineering model validation) so you can effectively build tools that serve them.

• Education:Masters or Bachelors degree in Computer Science Software Engineering Artificial Intelligence Data Science or a related technical discipline.
• AI/Engineering Experience:47 years of overall software engineering or AI development experience (Python PyTorch Pandas) with at least 12 years of dedicated hands-on experience building and deploying LLMs and GenAI applications into production.
• DevOps/MLOps:Proven track record of implementing CI/CD for machine learning/AI containerization (Docker/Kubernetes) and cloud infrastructure management.
• Good to Have (Optional):
  • Previous exposure to the Banking Financial Services or Credit Analytics industries.
  • Familiarity with SAS to aid in building code-translation or modernization tools for modelers.