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Role Overview: 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.

• 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.

• 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: Proficiency in Python and ML frameworks such as PyTorch or TensorFlow. Strong experience with AI, machine learning, and deep learning technologies as well as 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: Excellent verbal and written communication skills. Ability to communicate complex technical concepts to non-technical stakeholders and build strong working relationships across the organization.

• 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.

• Bachelor's or Master's degree in Computer Science, AI/ML, Data Science, or a related field. Ph.D. is a plus.