The SpringCube team curated the following job opportunity to help you in your job search. Explore the position below to find your next career move.
Company Overview
This company is a global professional services firm that provides a comprehensive suite of services to help businesses navigate the complexities of the digital age.
Job Description
Responsible AI – Technology Architecture Manager
Qualifications
- Education: Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or related technical fields.
- Experience:
- Manager experience: 6-8 years
- Senior Manager: 8-10 years
- Technical Expertise:
- Architectural experience with cloud platforms (e.g., AWS, Azure, Google Cloud) and their AI services
- Proficiency in AI and machine learning algorithms and frameworks (e.g., TensorFlow, PyTorch)
- Strong programming skills in languages such as Python, R, or Java
- Experience with big data technologies (e.g., Hadoop, Spark) and database management systems
- Specific technology domain areas experience in at least one of: software development, cloud computing, systems engineering, data & analytics
- AI & Ethics:
- Knowledge of ethical AI principles, data privacy laws, and regulatory requirements
- Project Management:
- Proven track record in leading and delivering complex AI projects
- Experience in implementing AI solutions in large-scale, enterprise environments
- Leadership & Communication:
- Strong leadership and team management skills
- Excellent problem-solving and analytical abilities
- Effective communication skills, capable of conveying complex AI concepts to diverse audiences
- Other:
- Commitment to ethical AI practices and continuous learning
- Ability to work under pressure and manage multiple projects simultaneously
Bonus Points
- Additional courses or training in areas related to responsible AI, ethics in technology, or similar fields are beneficial.
- Certifications in AI, machine learning, or related fields.
- Experience with Data & AI in specific domains (e.g., healthcare, finance, retail).
- Familiarity with AI model explainability and interpretability tools.
- Understanding of MLOps, LLMOps and DevOps practices, including CI/CD pipelines, for AI systems.
- Containerization capabilities such as Docker and Kubernetes.
- Linguistic proficiency (to a reasonable business level) in a language other than English
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