AI Software Reliability Engineer

At Lilly, we unite caring with discovery to make life better for people around the world. We are a global healthcare leader headquartered in Indianapolis, Indiana. Our employees around the world work to discover and bring life-changing medicines to those who need them, improve the understanding and management of disease, and give back to our communities through philanthropy and volunteerism. We give our best effort to our work, and we put people first. We’re looking for people who are determined to make life better for people around the world.

Come shape the future of our industry by bringing Artificial Intelligence capabilities to life!

Digital Core Tech@Lilly is actively looking for an AI Software Reliability Engineer to spearhead our efforts in enhancing the robustness and dependability of AI-driven systems across multiple processes and systems in pharma. In this role, you'll be at the heart of ensuring that AI technology is not only innovative but also reliable and safe for users across various domains. Are you passionate about artificial intelligence and the impact it can have across an entire industry? Are you a change agent who can influence organizations? If so, bring YOUR skills and talents to Lilly where you’ll have the chance to create an impact on the lives of patients!

What You'll Be Doing:

AI Reliability Strategy and Implementation: Develop and implement strategies to enhance the reliability, performance, and security of AI-driven systems, employing best practices in software engineering and reliability principles. Collaborate with AI researchers and software developers to integrate reliability into every phase of the AI development lifecycle.

Incident Management and Resolution: Lead efforts to identify, diagnose, and resolve system outages or disruptions, employing a comprehensive understanding of AI technologies and software infrastructure. Utilize data-driven approaches to analyze root causes and implement preventative measures. Lead and work with third party team members in the process.

Performance Optimization: Monitor and optimize the performance of AI systems, ensuring they operate efficiently under varying conditions. Implement robust testing frameworks to simulate different scenarios and stress tests for AI models.

Continuous Integration and Deployment (CI/CD) for AI: Enhance and maintain CI/CD pipelines for AI systems, ensuring smooth and reliable deployment of AI models and applications. Work closely with development teams to automate reliability checks and balances within the deployment process.

Reliability and Security Advocacy: Champion best practices in AI reliability and security within the organization. Provide guidance and training to development teams on creating more reliable and secure AI solutions. Stay abreast of emerging trends and technologies in AI reliability and cybersecurity.

How You Will Succeed:

Analytical Expertise and Innovation: Leverage strong analytical skills to tackle complex problems related to AI reliability, deploying innovative solutions that enhance system performance and robustness.

Technical Proficiency: Utilize your deep technical knowledge in software engineering, AI technologies, and reliability engineering principles to drive the development of reliable AI applications.

Effective Collaboration and Communication: Work effectively across multidisciplinary teams, communicating complex reliability concepts in an understandable manner to both technical and non-technical stakeholders.

Proactive Problem-Solving: Anticipate potential reliability issues in AI systems and address them proactively, employing a preventative approach to system design and maintenance.

Continuous Improvement: Embrace a culture of continuous improvement, constantly seeking ways to enhance the reliability and performance of AI systems. Engage in ongoing learning to stay at the forefront of AI reliability engineering.

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What You Should Bring:

Strong Background in Software Engineering: Demonstrated experience in software engineering, with a focus on reliability engineering principles. Knowledge of AI technologies and their implications for system reliability.

Proficiency in System Design and Analysis: Expertise in designing robust systems and conducting thorough system analysis to ensure reliability and performance.

Collaborative Spirit and Leadership Skills: Proven ability to lead and collaborate with cross-functional and vendor teams. Excellent communication skills to effectively convey reliability concepts and practices.

Innovative Thinking and Problem-Solving Ability: A track record of innovative problem-solving, with a focus on enhancing system reliability and security.

Commitment to Ethical and Continuous Learning: A dedication to ethical practices in AI development and a commitment to continuous learning and staying updated with the latest in AI reliability, security, and software engineering practices.

Your Basic Qualifications:

• Bachelor’s degree in computer science, engineering, mathematics, or a related field.

• Experience in software engineering with a specific focus on reliability and performance aspects.

• Experience with programming languages such as Python, Java, or C++, especially in the contexts of software troubleshooting, testing, deployment, and error handling.

Additional Preferences:

• ITIL Foundations certification with advanced certifications a plus

• DevSecOps certification and experience

• Experience in working with AI technologies and tools, such as natural language processing (NLP), AI generation, machine learning algorithms, deep learning, etc.

• Advanced proficiency in programming languages such as Python, Java, R, C++, or JavaScript, and familiarity with AI libraries and frameworks like TensorFlow, PyTorch, Keras, and Scikit-learn.

• Strong understanding of software development methodologies including Agile and Scrum, as well as expertise in version control systems like Git.

• Experience with containerization and orchestration technologies such as Docker and Kubernetes, and familiarity with CI/CD pipeline setup and management.

• Proficiency in data manipulation tools and libraries such as Pandas and NumPy, and experience with both SQL and NoSQL database technologies.

• Knowledge of advanced machine learning and deep learning techniques and frameworks, including but not limited to supervised and unsupervised learning models, neural networks, GANs, autoencoders, and transformers.

• Understanding of NLP techniques and practical experience with tools for text analysis such as NLTK, SpaCy, or Gensim.

Additional Information:

• Position located in Indianapolis, Indiana working in a hybrid model.

• Travel may be required periodically.

Lilly is dedicated to helping individuals with disabilities to actively engage in the workforce, ensuring equal opportunities when vying for positions. If you require accommodation to submit a resume for a position at Lilly, please complete the accommodation request form (https://careers.lilly.com/us/en/workplace-accommodation) for further assistance. Please note this is for individuals to request an accommodation as part of the application process and any other correspondence will not receive a response.

Lilly is an EEO/Affirmative Action Employer and does not discriminate on the basis of age, race, color, religion, gender, sexual orientation, gender identity, gender expression, national origin, protected veteran status, disability or any other legally protected status.

Our employee resource groups (ERGs) offer strong support networks for their members and help our company develop talented individuals for future leadership roles. Our current groups include: Africa, Middle East, Central Asia Network, African American Network, Chinese Culture Network, Early Career Professionals, Japanese International Leadership Network (JILN), Lilly India Network, Organization of Latinos at Lilly, PRIDE (LGBTQ + Allies), Veterans Leadership Network, Women’s Network, Working and Living with Disabilities. Learn more about all of our groups.

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