Manager, AI Engineering

Technical Responsibilities:

1. Understand complex business problems and implement product features that are secure, scalable, robust, and easy to implement and maintain for AI products.
2. Take full ownership of product features to implement, provide bug fixes and write tests and tooling for those features to ensure they work well at cloud scale.
3. Identifying and leading technical initiatives independently towards planned outcomes.
4. Collaborate with Data Scientists, Python Engineers, and Product Management to design and deliver product features.
5. Stay abreast of the latest AI/ML research and technologies and evaluate their applicability to business problems.
6. Oversee the end-to-end lifecycle of AI features - from ideation and prototyping to deployment and monitoring.

People Management Responsibilities:

1. Provide technical and career mentorship to team members; foster a culture of continuous learning and improvement.
2. Embrace an AI-first approach to enhance efficiency across all areas.
3. Manage and guide a team of AI Engineers, including performance evaluations, goal setting, and skill development.
4. Facilitate effective collaboration within the team and across cross-functional stakeholders (QA, Product, Architecture).
5. Promote a positive team environment, resolve conflicts, and support team engagement and motivation.
6. Support resource planning, workload distribution, and succession planning based on project and organizational needs.
7. Act as a point of escalation for team-related challenges and facilitate solutions in coordination with leadership.

Qualifications

1. Experience - 12-16 years with at least 4 years in AI/ML-focused roles and 2+ years in leadership capacity.
2. Exposure to Agile methodologies and DevOps practices.
3. Expertise on the Microsoft technology stack (ASP.NET MVC 3.5 / 4.0 / 4.5, Web API, SQL Server).
4. Good knowledge of Client-side scripting JavaScript and jQuery, Kendo, ReactJS.
5. Good knowledge of AI services and LLMs to deliver solutions.

6. Familiarity with responsible AI practices, model interpretability, and ethical AI considerations.

1. Good to have Python and familiarity with data engineering tools and cloud platforms (e.g., AWS, Azure, GCP).
2. Knowledge of design patterns is required.
3. Excellent communication skills with the ability to engage both technical and non-technical stakeholders.
4. Demonstrated experience with non-functional requirements like performance, scalability, and security.
5. Experience in AI Product Engineering is desirable.