Senior Artificial Intelligence Engineer Lead

As a seasoned leader in the field of Artificial Intelligence and Machine Learning, you will be responsible for guiding a team of engineers to create cutting-edge algorithms and solutions. Your strategic direction will ensure timely delivery of high-impact projects.

Key Responsibilities:

• Team Leadership and Management

• Lead and manage a team of AI/ML engineers and data scientists, setting clear goals, providing mentorship, and promoting a collaborative and growth-focused culture.

• Foster innovation and create an environment that encourages creative problem-solving and the application of state-of-the-art machine learning techniques.

• Conduct performance reviews, provide feedback, and guide career development for team members.

• Promote a collaborative team environment by facilitating team meetings, brainstorming sessions, and knowledge-sharing activities.

AI/ML Solution Design and Development

• Architect and design AI/ML models for a variety of use cases, such as predictive analytics, natural language processing (NLP), computer vision, recommendation systems, and optimization problems.

• Collaborate with product managers and stakeholders to understand business requirements and translate them into effective AI/ML solutions.

• Oversee the entire machine learning pipeline, including data collection, preprocessing, feature engineering, model selection, evaluation, and deployment.

• Develop and implement AI/ML algorithms and integrate them into production systems to solve complex business problems and enhance the overall user experience.

Model Training, Optimization, and Deployment

• Monitor and maintain model performance, ensuring that the deployed models remain accurate and efficient over time.

• Evaluate and improve existing AI/ML models, identifying areas for optimization and implementing solutions to enhance performance (e.g., accuracy, speed, scalability).

• Lead the deployment and integration of machine learning models into production environments, working closely with DevOps and infrastructure teams.

• Implement best practices for model versioning, monitoring, and continuous integration/continuous deployment (CI/CD).

Research and Innovation

• Stay up-to-date with industry trends and cutting-edge research in AI/ML and related fields.

• Evaluate and prototype new AI/ML technologies, tools, and frameworks that could improve the company's offerings or solve business problems more efficiently.

• Publish research papers, participate in conferences, or collaborate with academia to stay at the forefront of AI research and development.

Cross-Functional Collaboration

• Collaborate with cross-functional teams, such as engineering, product, and business teams, to ensure AI/ML solutions align with company objectives and technical requirements.

• Act as a key stakeholder in data strategy, ensuring that AI/ML initiatives are aligned with broader company goals and available data resources.

• Communicate complex AI/ML concepts to non-technical stakeholders in a clear, concise manner to support decision-making processes.

Documentation and Reporting

• Document AI/ML processes, methodologies, and algorithms for internal knowledge sharing and compliance purposes.

• Provide regular progress updates to senior leadership, highlighting key milestones, results, and challenges.

• Create comprehensive reports that include model performance analysis, business impact, and insights gained from AI projects.

Required Qualifications:

• Education: Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Mathematics, or a related field.

• Experience: 5+ years of experience working in AI/ML development, with a proven track record of designing, deploying, and managing machine learning models and solutions.

• Skills: Strong experience in modeling and designing machine learning solutions across various domains (e.g., NLP, computer vision, predictive analytics, etc.). Familiarity with big data tools and frameworks (e.g., Hadoop, Spark, Kafka) and knowledge of handling large datasets for training models.