AI Engineer/Data Scientist

Commercial Bank of California•Irvine, CA

9d•$170,000 - $220,000•Hybrid

About The Position

As a Senior AI Engineer/Data Scientist, you will lead the design, development, and deployment of production-grade AI solutions within our financial services ecosystem. You will bridge the gap between complex data science and scalable software engineering, utilizing Azure AI Foundry to build secure, high-performing models. A primary focus will be on Intelligent Automation—replacing manual financial workflows with AI-driven agents and LLM-powered systems.

Requirements

7+ years of experience in AI/ML engineering or data science with minimum 10+ IT Engineering experience.
Bachelor’s degree in computer science, Engineering, Mathematics, Data Science, Information Systems, or equivalent.
Proven experience with Prompt Engineering, RAG, and fine-tuning techniques.
Mastery of Azure AI Foundry, Azure Machine Learning, and Azure OpenAI Service.
Hands-on experience with Claude Code, GitHub Copilot, LangChain, and LlamaIndex for agentic workflows.
Expert-level proficiency in Python (PyTorch, TensorFlow, Scikit-learn) and SQL.
Strong understanding of MLOps (Machine Learning Operations) to manage model versioning, monitoring, and retraining.
Experience with CI/CD pipelines, containerization (Docker/Kubernetes), and API development (FastAPI/Flask).
Deep understanding of data engineering, integration, and pipeline architecture.

Nice To Haves

Direct experience in the financial industry, understanding the nuances of risk management, transaction monitoring, and payments is a plus.
Familiarity with financial data structures and the challenges of legacy banking system integration.
Familiarity with synthetic data generation for financial modeling
Understanding of real-time fraud detection architectures
Experience with conversational AI for banking (chatbots, virtual assistants)
Experience vetting potential use cases for AI capabilities, implementing viable approaches, and defining/tracking measures of success
Certifications such as Microsoft Certified: Azure AI Engineer Associate (AI-102) or Azure Data Scientist Associate (DP-100).
Prior experience in banking, financial services, or fintech (strongly preferred)
A portfolio of successful AI-driven automation projects that resulted in measurable ROI or time savings.

Responsibilities

Design and implement end-to-end AI pipelines using Azure AI Foundry (formerly Azure AI Studio), including orchestrating complex AI workflows, building RAG (Retrieval-Augmented Generation) systems for financial document processing, and deploying large language models. You’ll establish best practices for prompt engineering, model fine-tuning, and responsible AI implementation across the organization.
Identify and automate high-value banking processes using a combination of traditional ML, generative AI, and agentic systems. This includes automating loan origination workflows, regulatory reporting, document intelligence systems, customer service automation, and compliance monitoring using tools like Azure Document Intelligence, OpenAI models, Claude, and custom solutions.
Identify manual processes in banking operations (e.g., KYC, credit scoring, fraud detection) and engineer automated solutions using AI agents and RAG (Retrieval-Augmented Generation) architectures.
Leverage Claude Code and other developer-centric AI tools to accelerate the software development lifecycle (SDLC) and maintain high code quality.
Evaluate and fine-tune Large Language Models (LLMs) like GPT-4, Claude 4, and Llama 3 for finance-specific terminology and compliance requirements.
Work closely with cybersecurity teams to ensure all AI deployments adhere to banking regulations (GDPR, CCPA, and Basel III standards), implementing robust guardrails and "Human-in-the-Loop" systems.
Document solutions, present technical findings, and support knowledge sharing across teams.
Create production-ready AI systems by incorporating Enterprise-wide framework, rapidly adapt to emerging AI techniques, and own the lifecycle of models and agents from initial ideation through monitoring and maintenance. The engineer will coordinate with cross-functional data and business teams but retain technical accountability.
Own the complete AI development lifecycle: prototype, productionize, monitor, and continuously improve models and systems.
Rapidly evaluate, adopt, and optimize new AI techniques and platforms as technology evolves