Memphis or Boston
We are looking for a Lead Artificial Intelligence (AI) Engineer who wants to help solve research problems for the discovery and translation of biomedical knowledge into therapeutic treatments and cures for pediatric illness. The primary responsibility of Lead Artificial intelligence (AI) Engineer is to architect, design, develop, implement, and support the Information Services (IS) AI computing resources and AI-enabled services. This position (1) architects, evaluates, develops, and implements high-performance AI computing infrastructure resources, AI-enabled services, and AI application stacks, (2) provides consultation, design input, and feedback for AI use cases and solutions for business productivity and scientific discovery, (3) collaborates with scientists, data analysts, business stakeholders, and IS teams on all AI-related activities, (4) provides guidance on AI-related standards, best practices, and governance frameworks, and (5) supports the operation, and maintenance of the AI computing infrastructure ensuring that AI computing resources and AI-enabled services are delivered to the institution.
- Assists Vice President of Research Informatics in (i) assessing institution’s needs and (ii) developing strategic architecture and expansion roadmaps for AI infrastructure and AI-enabled services.
- Architect, design, develop, and implement scalable and high-performance computing infrastructure and application stacks for AI/Machine Learning (ML) and Data Mining.
- Leads and works with scientists, business stakeholders, analysts, and IS professionals in the design, development, and implementation of AI solutions and services, including technology proof-of-concepts, pilots, and the adoption lifecycle.
- Be responsible for building new and innovative solutions leveraging data science and AI/ML skills and technologies to solve non-trivial problems.
- Develops AI demonstration use-cases and workshop materials and provides workshops and training classes.
- Evaluate commercial and open-source approaches in AI/ML, Data Mining, and Analytics to solve business problems.
- Identifies, develops, and implements standards and operating procedures for solutions and systems consistent with best practices.
- Documents current and future state architecture roadmaps and reference architectures.
- Provides clear written and spoken communications to customers, teams, and vendors.
- Keeps abreast of new and emerging technologies and stays adaptable to their potential applicability.
- Identifies and develops disaster recovery and business continuity plans regarding AI computing infrastructure in collaboration with other IS teams and vendors.
- Performs other duties as assigned or directed to meet the goals and objectives of the department and institution.
- Master’s degree in computer science, computer engineering, data science, information technology or related field required.
- PhD degree in data science, computer science, computer engineering, information technology or related field preferred.
- 5 years in designing high level architectures and developing solutions for large scale AI/ML systems and/or building solutions for a product on AI/ML features and capabilities.
- Strong background in industry use cases built on deep learning and machine learning (unsupervised and supervised techniques) is a must.
- Deep learning frameworks such as TensorFlow, Keras, PyTorch, Time series analysis, anomaly detection, forecasting, predictive modeling, graph- based neural networks, Bayesian statistics, and text analytics are a must.
Special Skills, Knowledge and Abilities:
- Advanced knowledge and strong understanding of predictive analytics, AI/ML, and text analytics with natural language processing (NLP).
- Advanced knowledge and skills in deep learning architectures like RNN, CNN, LSTM and Transformers including generative AI and LLMs.
- Strong command of Python language for AI/ML; Graph databases, analytics, and graph neural networks; data structures and ability to develop efficient solutions.
- Advanced knowledge and skills in high-performance AI computing platforms running with Kubernetes and Machine Learning Operations (MLOps) for streamlining the process of taking AI/ML models to production, and then maintaining and monitoring them.
- Advanced knowledge and skills in using Linux and shell scripting in a high-performance AI computing environment is required.
- Excellent verbal and written communication skills; the ability to share technical results and recommendations with both technical and non-technical audiences.
- Ability to perform high-level work both independently and collaboratively as a project member or leader on multiple projects.
- Strategic systems capacity planning, updates, and migration of hardware and software to enhance computing capabilities and performance.
- Strong interpersonal skills with the ability to communicate technical issues and ideas to nontechnical personnel.