Mgr. Software Engineering-AI

UKG•Atlanta, GA

About The Position

As an Engineering Manager in the AI team, you will lead and mentor a team of 10+ software engineers working on a wide range of AI/ML/GenAI-driven initiatives. You will be responsible for the end-to-end delivery of AI solutions, guiding your team through the full software and machine learning lifecycle, from ideation and design to deployment and continuous improvement. In this hands-on leadership role, you will collaborate closely with engineering leaders, data scientists, product managers, and other stakeholders to deliver high-quality AI applications that power our products and services.

Requirements

Experience:
7–10 years of experience in software engineering, with at least 2–4 years in a leadership or management role overseeing AI or machine learning teams.
Proven track record of leading and delivering AI/ML and Generative AI initiatives at scale in fast-paced, dynamic environments.
Technical Skills:
Proficient in Python and ML frameworks such as PyTorch or TensorFlow.
Strong experience with AI, machine learning, and deep learning technologies.
Solid understanding of the software development lifecycle, object-oriented programming, concurrency, design patterns, RESTful services, and microservice architecture.
AI Tools and Cloud Technologies:
Experience with cloud platforms (AWS, Google Cloud, Azure) with a focus on AI/ML deployment (e.g., VertexAI, GKE, BigQuery, Kubeflow, TensorFlow Serving).
Familiarity with MLOps practices and tools for model deployment and monitoring (e.g., Terraform, GitHub Actions, Concourse).
Leadership and Collaboration:
Strong people management and leadership skills, with experience leading cross-functional teams.
Proven ability to mentor engineers, manage competing priorities, and align team efforts with business objectives.
Agile Experience:
Strong understanding of agile methodologies, with experience leading teams in an agile environment.
Knowledge of best practices for managing sprints, tasks, and project timelines.
Communication skills:
Excellent verbal and written communication skills.
Ability to communicate complex technical concepts to non-technical stakeholders and build strong working relationships across the organization.

Nice To Haves

Experience working with large language models (LLMs) or fine-tuning open-source models like GPT.
Familiarity with modern AI development tools such as Jupyter notebooks, Kubernetes, Docker, and CI/CD pipelines.
Experience in high-compliance or enterprise-grade environments with a focus on AI ethics, privacy, and security.
Education:
Ph.D. is a plus.

Responsibilities

Leadership and Mentorship:
Lead, mentor, and develop a team of 10+ AI/ML software engineers, fostering a culture of growth, collaboration, and technical excellence. Ensure high performance by providing regular feedback, coaching, and career development opportunities.
AI/GenAI Architecture and Delivery:
Oversee the design and delivery of AI and generative AI systems, ensuring that all solutions are scalable, robust, and high-quality. Guide your team to implement innovative AI models, tools, and infrastructure that drive business outcomes.
Cross-Functional Collaboration:
Partner with other engineering leaders, data scientists, product managers, and directors to align on strategic goals and deliverables. Collaborate closely with cross-functional teams to break down complex engineering tasks and drive the execution of the AI roadmap.
Agile Leadership:
Lead your team in an agile environment, participating in sprint planning, daily stand-ups, code reviews, and retrospectives. Help the team manage competing priorities while maintaining focus on delivering high-quality AI solutions.
Technical Excellence:
Promote best practices in software development, code quality, testing, and deployment. Drive the adoption of modern engineering practices like CI/CD, automated testing, and cloud-based AI/ML solutions to ensure the delivery of secure, scalable AI applications.
Innovation:
Stay up to date with the latest advancements in Generative AI and machine learning. Foster a culture of continuous learning, encouraging engineers to grow their technical skills and experiment with emerging technologies to contribute to the company’s thought leadership in AI.
Stakeholder Communication:
Act as a liaison between the AI team and other stakeholders, effectively communicating progress, risks, and technical insights to non-technical leadership and product teams.