Senior AI Engineer

Primary Purpose

The Senior Artificial Intelligence Engineer is a hands-on technical role that designs, implements, delivers, and maintains AI and Machine Learning (ML) products and platform. Working within a team, delivers ML/AI solutions supporting corporate business objectives by analyzing AI/ML use-cases, integrating various open source AI/ML libraries for model training, operationalizing models, supporting and monitoring models for retraining. May work on more complex products, such as those with higher level of dependencies with other teams, demonstrating a strong ability to solve challenging and unique technical problems facing the team.

- Deliver AI and ML products together with a multi-disciplinary team of data scientists, software development engineers, and domain subject matter experts in accordance with business acceptance criteria. Test deliverables against a user story’s acceptance criteria. Actively architect, develop, deliver and support machine learning solutions. Ensure the Machine Learning code, models and pipelines are deployed successfully into production, and troubleshooting issues that arise. Automate model training, testing and deployment via machine learning continuous delivery pipelines. Establish meaningful criteria for evaluating algorithm performance and suitability. Implement working, scalable, production-ready Machine Learning and AI models and code. Optimize processes for maximum speed, performance and accuracy. Maintains technical skills and expertise through continuing education and training.

- Deliver end-to-end maintenance and support of all products and features owned by the product team, performing triage and responding to incidents as needed. Identify opportunities for automation and integration for continuous improvement.

- Prepares for and leads reviews, walkthroughs, and demos of technical specifications and program code with other technical team members, communicating design, acceptance criteria, feature set, functionality, and limitations of applications to customers. Supports knowledge sharing amongst the team, and the development of other engineers in their technical skills through feedback, mentoring, paired programming, etc. Participate with IT staff, business/vendor partners, and other stakeholders in new product reviews, tests, and pilots. Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc. Serves as a thought leader for further innovation and strategic opportunities. Stay current with the latest development in AI and ML best practices, algorithms, tools, and processes.

- Work in alignment with agile mindset and values, working off user stories within an agile scrum team. Provides technical input into sprint planning, breaking down user stories and creating estimates, and planning to deliver within the sprint. Understands team dependencies and delivery impediments to proactively partner with other teams for effective delivery. Anticipates potential challenges that exist within the team's area of ownership. Participates in team's agile ceremonies, utilizing the five ceremonies involved in Scrum methodology to self-organize within their team and collaboratively drive development and the delivery of business value. Participate in quarterly planning and related demos/activities. Foster and maintain trusted relationships with business partners, IT peer teams and vendor partners. Collaborates with internal and external team members across the technology organization. Guides more junior AI engineers.

- Performs other duties as assigned (no more than 5% of duties).

Education

- Requires a Bachelor's Degree in Electrical Engineering, Computer Engineering or Computer Science, Physics, or Statistics or equivalent work experience.

Experience

- Requires 5 years of progressive experience as an AI engineer or equivalent, delivering IT solutions across one or more products, models and/or platforms: work with system integration architectures, cloud architectures, implement AI and machine learning products from design to deployment.

- 1 Year of experience working with DevSecOps practices, integrating development, security, and operations into enterprise software development, e.g., Continuous Integrations / Continuous Delivery (CI/CD) pipelines, test automation, etc. preferred.

- 1 Year of experience using source code management tools such as Git preferred.

Knowledge, Skills and Abilities

- Software Delivery Frameworks - Strong knowledge of delivery frameworks such as Agile Scrum, Kanban, and/or Software Development Lifecycle (SDLC). Proven ability executing projects in a collaborative, fast-paced environment.

- Machine Learning Development – Ability to develop machine learning models: architect, develop, deliver and monitor machine learning solutions and optimize for speed, performance, and accuracy.

- Development Languages - Knowledge and understanding of one or more IT programming languages and database architectures, and ability to write code and develop applications using those languages.

- Solid programming skills with Python, SQL, JavaScript or other equivalent languages

- Working knowledge of Machine Learning, Data Mining, Information Retrieval, Statistics

- Experience with cloud native data services in one or more cloud platforms (AWS/GCP/Azure)

- Understanding of common statistical modeling techniques to include but not limited to: decision trees, cost functions, gradient descent, linear regression, logistic regression, Bayesian analysis, and neural networks.

- Experience with one or more common machine learning frameworks: Tensorflow, Scikit Learn, Pytorch, or Keras

Licenses and Certifications

- AWS Cloud Practitioner preferred

- AWS Certified Solutions Architect preferred

- AWS Certified Machine Learning preferred

Equal Opportunity Employer Minorities/Women/Protected Veterans/Disabled