Lead AI Developer

About the job

The purpose of this role is to develop best in class strategies and management of all Insights and Analysis activity on assigned clients, to manage and develop the team and serve as a point of escalation as needed. Job Description: Key Responsibilities Team Leadership & Management Lead and manage a team of 5-10 AI engineers, ML engineers, and data scientists Conduct performance reviews, provide mentoring, and support career development Foster a collaborative culture focused on innovation and continuous learning Manage team workload distribution and resource allocation Technical Leadership Design and develop AI models and algorithms from scratch. Implement AI solutions that integrate with existing business systems to enhance functionality and user interaction. Provide technical guidance on AI architecture, model selection, and implementation strategies. Review code, ensure best practices, and maintain high technical standards. Drive technical decision-making for AI platform architecture and tooling. Stay current with AI/ML trends and evaluate new technologies for adoption. Project Management & Delivery Plan, execute, and deliver AI projects from conception to production deployment. Coordinate cross-functional initiatives with product, engineering, and business teams. Manage project timelines, budgets, and resource requirements. Ensure AI solutions meet scalability, performance, and reliability standards. Implement and maintain CI/CD pipelines for ML model deployment. Strategic Planning & Innovation Develop AI engineering roadmaps aligned with business objectives. Identify opportunities to leverage AI for business value and competitive advantage. Establish engineering best practices, coding standards, and quality assurance processes. Drive innovation initiatives and research into emerging AI technologies. Contribute to AI strategy discussions with senior leadership. Required Technical Skills AI/ML Expertise Python Strong background in machine learning algorithms, deep learning, and neural networks. Experience with ML frameworks: TensorFlow, PyTorch, scikit-learn, Keras. Knowledge of computer vision, NLP, and statistical modeling techniques. Data Engineering & Infrastructure Experience with data pipeline development and ETL processes. Knowledge of big data technologies (Spark, Hadoop, Kafka). Understanding of database systems (SQL, NoSQL, vector databases). Data preprocessing, feature engineering, and data quality management. Cloud & DevOps Experience with cloud platforms (AWS, Azure, GCP) for AI/ML workloads. MLOps tools and practices for model lifecycle management. Containerization and orchestration (Docker, Kubernetes). Continuous Deployment (CD). Experience Requirements 8+ years of AI/ML engineering experience with hands-on model development and deployment. 3+ years of team leadership or technical management experience. Proven track record of delivering production AI systems at scale. Experience managing the complete ML lifecycle from research to production. Background in both individual contribution and team collaboration.

Requirements

• AI/ML Expertise
• Technical Leadership
• Project Management
• Python
• Machine Learning
• Data Engineering
• Cloud

Qualifications

• 8+ years of AI/ML engineering experience
• 3+ years of team leadership experience
• Strong background in machine learning algorithms

Preferred Technologies

• AI/ML Expertise
• Technical Leadership
• Project Management
• Python
• Machine Learning
• Data Engineering
• Cloud