Machine Learning Engineer Tech Lead 2

Everything we do is about making the future of human work more purposeful. We do this by leveraging state-of-the-art machine learning, modern architectures, and industry experts, to create AI-powered solutions that transform the way we do business. 

As a member of AI Engineering, you are a builder excited to realize an AI idea’s impact at scale, distinguishing Crowe in the market and driving the firm’s technology and innovation strategy. 

About the Team 

• We foster good science. You are given the time and resources to build your expertise in your project and beyond; you will guide fellow MLEs and product stakeholders through your work. 

• We want to make good scientists better. MLEs have plenty of options on the job market. We want to be one of the best. We have regular ethics and book club meetings, monthly lightning talks where team members teach each other, support for conferences, and weekly “10P” independent time for ongoing learning.  

• We truly value work-life balance. Our distributed team offers flexible PTO and work-from-home policies.  We stress the importance of work-life balance and encourage you to be you.

About the Role 

• As a Tech Lead in AI Engineering, your responsibilities will vary across the development lifecycle, and include providing solutions architecture and vision for existing solutions, including the support of new feature development, testing, and preparing for release of high-quality, deployment-ready code. 

• AI Engineering Tech Leads are responsible for architecting the transition of AI projects from Proof of Concept to robust, production-ready solutions that transform our business offerings.  

• Conduct lightweight sprint planning and review sessions, prioritizing features and managing project timelines. 

• Write, review, and ensure the quality of production-ready code, setting high standards for the team. 

• Oversee the implementation of testing protocols, including smoke tests and unit tests, to maintain high-quality outputs, in collaboration with members of the MLOps software engineers. 

• Prepare and refine Service Level Agreements (SLAs) and release documentation in preparation for production deployment; collaborate with Enablement team to create user upskilling materials that support effective use and drive adoption of AI solutions and latest features. 

• Lead ethical reviews and risk/security evaluations to ensure compliance and safeguard our solutions. 

• Engage with business stakeholders to refine project objectives and demonstrate the value and impact of implemented features. 

• Act as a career coach, fostering the growth and development of team members through knowledge-sharing, constructive code review, and by setting the standard for code quality. 

Required Skills 

• Programming experience. You’re experienced writing scalable, production-level code in Python. You’re familiar with Linux/UNIX systems. 

• Machine learning experience. You’re proficient in machine learning packages, such as Tensorflow and Pytorch, and have proven expertise in designing/developing AI/ML models. You understand the mechanics of supporting ML solutions in a production environment and can help develop/maintain tests that validate functionality and evaluate model performance. 

• Software Experience. You’re familiar with the software development lifecycle, and ideally tenets of MLOps. You have exposure to CI/CD frameworks and tools like Docker and Git. You’re experienced deploying code in production environments. 

• Communication. Excellent communication skills, capable of effectively documenting and summarizing technical details for non-technical stakeholders. 

• Agile experience. You’re experienced attending Scrum or Kanban meetings; you favor incremental, iterative improvements through regular releases, testing, and monitoring. 

Preferred Skills 

• GenAI Experience. Expertise in prompt engineering and experience with tools like Enterprise ChatGPT, Microsoft Copilot, and Azure AI Studio. 

• Knowledge sharing. You enjoy sharing what you learn, whether by offering cross-training opportunities, giving internal team “lightning talks,” or by writing detailed comments on tickets when you close them. 

• Curiosity about machine learning. You want to stay fresh in machine learning. You aren't afraid to take a stab at deep learning papers before they become blog posts (even if you don't have experience in it quite yet). 

• Professional Services experience. You’re familiar with the landscape of a professional services firm and interested in engaging with the unique value proposition of products associated with a diverse range of services, from advising and consulting to tax and public accounting.