AI Engineering Manager

Proudly voted a Great Place to Work®, we are a dynamic startup in the SaaS space that is revolutionizing the way businesses communicate. Our team is made up of 500 energetic and passionate Unifones who are dedicated to delivering the best possible experience to 5000+ customer-centric companies

We pride ourselves on our fun and collaborative work environment, where creativity and new ideas are constantly encouraged. As shareholders in the business, we’re so much more than a group of passionate communicators. We are Unifones. Join our team and be a part of something big!

The AI Engineering Manager will play a pivotal role in shaping and executing our AI initiatives by bridging technical leadership with strong people management

Meet the team!

Our Engineering team is responsible for designing, developing, and maintaining the systems and technologies that drive Unifonic’s solutions. We work closely with other departments to ensure our products and services meet the needs of our customers. If you are passionate about technology and are excited about working on cutting-edge communication and engagement solutions, we want you on our team.

As an AI Engineering Manager, you will be responsible for managing and developing a team of highly skilled AI engineers while also remaining hands-on in the technical work. This dual focus ensures that you will guide your team to deliver high-quality AI solutions while actively contributing to architectural decisions, code reviews, performance optimizations, and proof-of-concept developments. You will foster a culture of continuous learning, technical excellence, and collaboration, ensuring that both technology and talent thrive under your leadership.

Help us shape the future of communication by:

• Serving as the technical authority on AI model design, architecture, and integration within the product ecosystem.

• Leading the evaluation, selection, and implementation of AI/ML frameworks, tools, and best practices, ensuring scalability, robustness, and maintainability.

• Participating in hands-on coding, solution prototyping, and code reviews to maintain high quality standards and guide the team through complex technical challenges.

• Overseeing and refining the AI development lifecycle, including model training, validation, deployment, and ongoing improvement.

• Leading a team of AI engineers, providing mentorship, regular feedback, and career development support.

• Defining clear performance expectations, conduct performance reviews, and identify growth opportunities for team members.

• Fostering a positive, inclusive, and high-performance team culture that encourages innovation, continuous learning, and collaboration

• Collaborating with recruitment and HR to identify, attract, and retain top AI engineering talent, ensuring the team’s ongoing growth and success

• Leading a team of AI engineers, providing mentorship, regular feedback, and career development support

• Defining clear performance expectations, conduct performance reviews, and identify growth opportunities for team members

• Fostering a positive, inclusive, and high-performance team culture that encourages innovation, continuous learning, and collaboration

• Collaborating with recruitment and HR to identify, attract, and retain top AI engineering talent, ensuring the team’s ongoing growth and success

• Working closely with the Director of AI Development, Product Managers, Designers, Data Scientists, and other Engineering leaders to translate business requirements into technical roadmaps and actionable engineering plans

• Ensuring seamless integration of AI capabilities into existing and future products, partnering with platform, infrastructure, and DevOps teams to optimize deployment and operations

• Communicating technical topics effectively to non-technical stakeholders, making recommendations and reporting progress, risks, and opportunities

• Implementing and continuously improving the development processes, standards, and tools that drive efficiency, reliability, and scalability.

• Ensuring adherence to best practices in model governance, performance monitoring, and compliance with relevant data privacy and security regulations

• Monitoring engineering metrics and KPIs, leveraging data-driven insights to improve development velocity, code quality, and team efficiency