Lead Engineer, AI Software

Essential Duties and Responsibilities:

- Design and develop advanced AI software applications, ensuring scalability, performance, and integration with enterprise systems. - Responsible for developing and modifying systems software (i.e., software for use in operating/controlling hardware and providing a platform for running internal end-user/business applications). - Develop and communicate descriptive, diagnostic, predictive and prescriptive insights/algorithms. - Apply knowledge of experimental methodologies, statistics, optimization, probability theory and machine learning using code for tool building, statistical analysis and modeling, using both general purpose software and statistical languages. - Lead technical implementation efforts in both theoretical and product design environments, translating complex algorithms and specifications into code while mentoring junior engineers and guiding team-level development practices.

Job-Specific Essential Duties and Responsibilities:

- Design, build, ship, and operate production software systems incorporating AI and machine learning capabilities.

- Own difficult technical problems from initial exploration through implementation, deployment, troubleshooting, and continuous improvement.

- Write high-quality software and contribute directly to application code, APIs, services, automated tests, infrastructure, and engineering tooling.

- Build AI-enabled applications that integrate models, enterprise data, cloud services, APIs, and existing systems.

- Apply strong engineering practices including automated testing, CI/CD, observability, security, resiliency, and performance optimization.

- Evaluate emerging AI technologies through hands-on experimentation and determine where they can provide practical value in production.

- Build reusable components, libraries, patterns, and reference implementations that help other teams deliver better software.

- Mentor other engineers through code reviews, pairing, design discussions, and shared problem solving.

Minimum Requirements

- Bachelor's degree in relevant field of study and 7+ years of relevant professional experience required, or equivalent combination of education and experience.

Job-Specific Minimum Requirements:

- 7+ years of professional software engineering experience, including substantial experience building and supporting production systems.

- 3+ years of experience with artificial intelligence, machine learning, or related technologies.

- 5+ years of experience designing or building solutions in cloud environments.

- Demonstrated experience owning software through deployment, production operations, troubleshooting, maintenance, and evolution.

- Strong software engineering fundamentals, including system and API design, testing, source control, and secure development.

- Experience designing and implementing systems on AWS, Azure, GCP, or similar cloud platforms.

- Experience with modern software delivery practices including automated testing, CI/CD, infrastructure automation, and observability.

- Experience building or integrating AI/ML capabilities into software systems or demonstrated ability to quickly learn and apply emerging technologies.

- Ability to communicate technical decisions, tradeoffs, and risks clearly with technical and non-technical colleagues.

- US Citizenship required.

- Must have the ability to obtain a Public Trust.

Preferred Skills and Qualifications:

- Deep expertise in Python, demonstrated through substantial production software rather than scripts, notebooks, or prototypes alone.

- Proficiency in at least one systems programming language, such as Rust, Go, C++, or C, with experience applying it to real-world software engineering problems.

- Experience building and operating distributed or cloud-native production systems.

- A track record of taking systems from initial development through production and ongoing operation.

- Experience diagnosing complex production problems across application, infrastructure, data, and integration boundaries.

- Experience designing APIs, services, asynchronous workflows, and system integrations.

- Practical experience building applications using large language models, machine learning models, or managed AI services.

- Experience working in environments where security, privacy, reliability, or regulatory requirements influence engineering decisions.

- Experience improving existing systems and leaving codebases safer, simpler, more reliable, or easier to operate.

- Experience mentoring engineers through collaboration, code reviews, and technical problem solving.

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