AI Engineer

Responsibilities

• Develop and deploy AI applications and integrations using Azure AI services, LLM APIs, Microsoft Power Platform, and third-party AI tools that enhance business productivity.
• Fine-tune and customize AI models to improve accuracy, relevance, and performance for specific business use cases across origination, closing, and servicing workflows.
• Monitor and optimize performance of AI systems, ensuring quality, responsiveness, and cost efficiency in production environments.
• Collaborate with AI Solutions Architect, Business Partners, and end users to understand workflows and deliver usable, intuitive AI-powered tools.
• Contribute to AI governance frameworks, documentation, and continuous learning around responsible AI use and best practices.
• Support citizen developers by creating AI components, templates, and integrations that enable business users to build AI-enhanced solutions.
• Partner with AIOps Engineer to implement monitoring, alerting, and optimization strategies for AI applications and services.
• Participate in rapid prototyping and proof-of-concept development to validate AI use cases and technical feasibility.
• Implement security and compliance controls for AI applications ensuring adherence to financial services regulations and data privacy requirements.
• Research and experiment with emerging AI technologies and techniques to enhance BWE's AI capabilities and competitive advantage.
• Maintain and update AI application code, deployment pipelines, and integration frameworks as technologies evolve.
• Provide technical support and troubleshooting for deployed AI solutions across business functions.

Requirements

• 3–5 years of experience in AI/ML engineering, automation, software development, or related technical roles.
• Proficiency with Python and experience working with APIs, Large Language Models (LLMs), and cloud-based AI services.
• Strong familiarity with Microsoft ecosystem including Azure AI, Power Automate, Power Apps, and Copilot platforms.
• Experience with AI model deployment, monitoring, and optimization in production environments.
• Knowledge of software development best practices including version control, testing, and deployment automation.
• Understanding of data processing, ETL workflows, and integration with enterprise systems.
• Bachelor's degree in Computer Science, Engineering, Data Science, or related technical field.
• Ability to balance experimentation and innovation with structured, scalable deployment practices.

Preferred Qualifications

• Experience with multiple AI platforms beyond Microsoft ecosystem (OpenAI, AWS AI, Google Cloud AI).
• Knowledge of AI model fine-tuning, training, and optimization techniques for enterprise applications.
• Familiarity with DevOps practices, containerization (Docker, Kubernetes), and CI/CD pipelines for AI applications.
• Experience in CRE, financial services, or regulated industries with compliance and security requirements.
• Understanding of AI governance, model risk management, and responsible AI deployment practices.
• Knowledge of natural language processing, computer vision, or other specialized AI domains.
• Experience supporting citizen development initiatives and low-code/no-code platform development.