Management-Jobs in United States

Job Summary:
The AI Development Team Lead is responsible for managing and mentoring a team of AI engineers, ML developers, and data scientists. This role bridges strategic objectives from the Head of Department (HOD) with execution by the technical team. The ideal candidate combines technical AI/ML expertise with project management and people leadership to ensure the successful delivery of AI initiatives that align with business goals.

Key Responsibilities:
Strategic Planning & Execution
• Collaborate with HOD to define AI vision, goals, and KPIs aligned with business needs.
• Translate high-level strategy into actionable technical tasks and sprint plans.
• Evaluate and recommend AI technologies, platforms, tools, and best practices.
Team Leadership & Management
• Lead, mentor, and manage a multidisciplinary team (AI engineers, ML specialists, data scientists).
• Conduct regular one-on-ones, performance reviews, and skill gap assessments.
• Promote a collaborative, transparent, and accountable team environment.
Technical Oversight
• Oversee design, development, testing, deployment, and maintenance of AI/ML models.
• Perform code reviews, model validation, and ensure adherence to engineering standards.
• Identify and resolve technical bottlenecks, support experimentation and innovation.
Project Management
• Manage project timelines, resource allocation, risk assessments, and reporting.
• Facilitate Agile ceremonies (sprint planning, stand-ups, retrospectives).
• Monitor and report on progress, blockers, and deliverables to HOD and stakeholders.
Cross-Functional Collaboration
• Work closely with product managers, data engineers, software teams, and business stakeholders.
• Ensure AI models and systems integrate well with broader platforms or customer touchpoints.
• Translate business questions into data science problems and actionable models.
Governance, Ethics & Compliance
• Ensure all AI/ML developments comply with data privacy laws and AI ethics standards.
• Define policies for model retraining, versioning, and explainability.

Qualifications & Skills:
Education:
• Bachelor’s or Master’s in Computer Science, Artificial Intelligence, Data Science, or related fields.
• Certifications in AI/ML (e.g., Google AI, AWS ML, Microsoft AI Engineer) are a plus.
Experience:
• 5–8 years total in AI/ML, with 2–3 years in a leadership or technical management role.
• Proven experience in deploying ML models into production (real-time or batch).
• Familiarity with Agile/Scrum practices and tools like Jira, Confluence, Git, etc.
Technical Proficiency:
• Proficient in Python and ML libraries: TensorFlow, PyTorch, scikit-learn, etc.
• Experience with model lifecycle: training, tuning, deployment, and monitoring.
• Strong understanding of supervised/unsupervised learning, NLP, computer vision, or generative AI.
• Familiarity with MLOps: Docker, Kubernetes, MLFlow, CI/CD for AI pipelines.
• Experience working with cloud platforms: AWS, GCP, Azure.
Soft Skills:
• Excellent communication and stakeholder management skills.
• Strong leadership, team-building, and conflict resolution skills.
• Analytical mindset with attention to both big-picture strategy and technicaldetail.