WE ARE COGNIZANT ARTIFICIAL INTELLIGENCE

Digital technologies, including analytics and AI, give companies a once-in-a-generation opportunity to perform orders of magnitude better than ever before. But clients need new business models built from analyzing customers and business operations at every angle to really understand them.
With the power to apply artificial intelligence and data science to business decisions via enterprise data management solutions, we help leading companies prototype, refine, validate, and scale the most desirable products and delivery models to enterprise scale within weeks.
*You must be legally authorized to work in United States without the need of employer sponsorship, now or at any time in the future *
This is a remote position open to any qualified applicant in the United States
Job Title: Chief Gen AI Architect (Remote)

JOB SUMMARY:

As a Generative Artificial Intelligence Architect (Gen AI), your primary role encompasses to design and develop artificial intelligence systems that have the capability to learn, reason, and make decisions autonomously. Your main responsibilities entail the architecture and infrastructure required to build and deploy AI technologies that can adapt and evolve based on various data inputs and user interactions. Your expertise in AI algorithms, machine learning, and neural networks will be essential in creating intelligent systems that can address sophisticated challenges.

QUALIFICATIONS:

• A bachelor’s or master’s degree or equivalent in computer science, Artificial Intelligence, or related field.
• Validated experience in designing and developing AI systems, including machine learning, deep learning, and neural networks.
• Strong programming skills in languages such as Python, R, or Java. Familiarity with AI libraries, frameworks, and tools such as TensorFlow, PyTorch, or Keras.
• Proven understanding of cloud computing platforms (e.g., AWS, Azure, Google Cloud) and experience deploying AI models on these platforms.
• Solid understanding of AI concepts, algorithms, and methodologies.
• Knowledge of designing large scale AI solutions, data integration, cleansing, and transformation techniques.
• Familiarity with BigData platforms and technologies, such as Hadoop or Spark.
• Excellent problem-solving and analytical skills, with the ability to think creatively and provide innovative solutions.
• Strong communication and collaboration skills to work effectively in multidisciplinary teams.
• Knowledge of ethical AI practices and laws is a plus.

• AI System Architecture: Design and develop the architecture and infrastructure for AI systems, including data storage, processing, and retrieval mechanisms. Ensure scalability, flexibility, and efficiency of the system to handle large volumes of data and sophisticated algorithms.
• Algorithm Development: Develop and implement sophisticated AI algorithms and models, including machine learning, deep learning, and neural networks. Continuously evaluate and improve algorithms to enhance system performance and accuracy.
• Data Integration: Identify relevant data sources and design methods for data collection, integration, cleansing, and transformation. Collaborate with data scientists and engineers to ensure quality and relevance of data for AI model training.
• Model Training and Evaluation: Train AI models using supervised, unsupervised, or reinforcement learning techniques. Implement evaluation methodologies to measure the performance and effectiveness of trained models. Fine-tune models based on feedback and data insights.
• Neural Network Design: Design and optimize deep learning neural networks for various AI tasks, such as natural language processing, computer vision, recommendation systems, and predictive analytics. Implement innovative architectures and techniques to improve model accuracy and efficiency.
• System Integration: Collaborate with software developers and engineers to integrate AI systems into existing platforms or applications. Ensure flawless communication and compatibility between AI components and other software modules.
• Ethical and Responsible AI: Ensure consistency to ethical AI practices, such as fairness, transparency, and accountability. Address biases and potential risks associated with AI systems to ensure responsible deployment and usage.
• Research and Innovation: Stay updated with the latest advancements in AI technologies, frameworks, and algorithms. Conduct research and experimentation to explore innovative approaches and techniques that can enhance AI capabilities.
• Teamwork and Communication: Collaborate with multi-functional teams, including data scientists, software engineers, and business customers, to define AI requirements and deliver AI solutions that meet business objectives. Communicate sophisticated AI concepts and solutions optimally to both technical and non-technical customers.