We are seeking a highly experienced and results-driven AI/ML Team Lead to drive the development, execution, and delivery of Artificial Intelligence and Machine Learning solutions across multiple business functions and projects. The successful candidate will be responsible for leading a team of AI/ML Engineers, Data Scientists, MLOps Engineers, and AI Developers while ensuring the successful execution of strategic AI initiatives.
The role requires a combination of strong technical expertise, people leadership, project management, stakeholder engagement, and business acumen. The AI/ML Team Lead will play a key role in defining the organization's AI roadmap, implementing best practices, driving innovation, and ensuring the delivery of scalable, secure, and production-ready AI solutions.
This position is ideal for someone who thrives in a fast-paced environment, enjoys solving complex business problems through AI, and has a proven track record of managing multiple projects and technical teams simultaneously.
AI Strategy & Leadership
- Define and execute the organization's AI and Machine Learning strategy aligned with business goals.
- Lead AI transformation initiatives and identify opportunities where AI can create measurable business value.
- Collaborate with executive leadership to develop long-term AI roadmaps and innovation strategies.
- Establish technical governance, standards, frameworks, and best practices for AI development.
- Promote a culture of innovation, experimentation, knowledge sharing, and continuous improvement within the AI team.
- Stay updated with emerging technologies, industry trends, research advancements, and best practices in AI, ML, GenAI, and Data Science.
Team Leadership & People Management
- Lead, mentor, coach, and develop a team of AI Engineers, Machine Learning Engineers, Data Scientists, Prompt Engineers, and MLOps professionals.
- Manage team performance through regular feedback, performance reviews, and career development planning.
- Foster a collaborative and high-performing team culture.
- Support hiring, onboarding, and retention of top AI talent.
- Conduct technical mentoring sessions and encourage continuous learning through certifications, workshops, and research activities.
- Allocate resources effectively across multiple projects and business priorities.
Multi-Project Management & Delivery Oversight
- Manage and oversee multiple AI/ML projects simultaneously from concept to deployment.
- Establish project goals, deliverables, milestones, timelines, and success metrics.
- Ensure projects are delivered on time, within scope, and aligned with business expectations.
- Monitor project progress, identify risks, and implement mitigation strategies.
- Prioritize tasks and manage competing business priorities effectively.
- Coordinate project execution across Data Engineering, Software Engineering, Product, QA, and Business teams.
- Provide regular status updates and executive reports to senior stakeholders.
AI/ML Solution Architecture & Development
Lead the design and implementation of scalable AI/ML systems and architectures.
Review and approve technical designs, model architectures, and deployment strategies.
Guide teams in developing solutions involving:
- Machine Learning
- Deep Learning
- Natural Language Processing (NLP)
- Computer Vision
- Generative AI
- Large Language Models (LLMs)
- AI Agents
- Recommendation Systems
- Predictive Analytics
- Intelligent Automation
Ensure solutions are scalable, secure, maintainable, and production-ready.
Establish best practices for model training, testing, validation, deployment, and monitoring.
Generative AI & Large Language Models
Lead enterprise adoption of Generative AI solutions and LLM-powered applications.
Design and oversee implementation of:
- RAG (Retrieval-Augmented Generation)
- AI Assistants
- Chatbots
- Multi-Agent Systems
- Knowledge Management Platforms
- Intelligent Document Processing Solutions
Evaluate and optimize foundation models including OpenAI, Azure OpenAI, Claude, Gemini, Llama, and other leading AI technologies.
Implement prompt engineering and model optimization strategies.
Develop AI governance frameworks for responsible AI implementation.
MLOps & Production Deployment
- Establish MLOps frameworks and best practices.
- Design CI/CD pipelines for AI model deployment and monitoring.
- Implement automated model retraining, performance tracking, and monitoring processes.
- Ensure proper version control for datasets, models, and experiments.
- Lead deployment of AI applications across cloud and on-premise infrastructure.
- Manage model lifecycle management and continuous improvement initiatives.
Stakeholder & Business Collaboration
- Work closely with business leaders to understand strategic objectives and identify AI opportunities.
- Translate business requirements into AI solutions and technical roadmaps.
- Communicate technical concepts effectively to both technical and non-technical stakeholders.
- Present project progress, ROI, and business impact to senior management and executives.
- Serve as the primary point of contact for AI initiatives across the organization.
Data Strategy & Governance
- Collaborate with Data Engineering teams to ensure data quality, availability, and accessibility.
- Establish data governance, security, privacy, and compliance standards.
- Ensure responsible handling of sensitive and confidential information.
Research & Innovation
- Drive innovation through research, experimentation, and proof-of-concept development.
- Evaluate new AI frameworks, tools, and emerging technologies.
- Establish AI Centers of Excellence and knowledge-sharing programs.
- Encourage participation in AI communities, conferences, and research initiatives.