Job Title:
AI-ML Sr Architect (Gen AI)
As a Lead GenAI Architect will design, implement and deliver cutting-edge solutions in the field of Generative Artificial Intelligence (GenAI) having experience in the R&D Clinical Experience. Job Description:
Primary role is to design and develop artificial intelligence systems that have the capability to learn, reason, and make decisions autonomously. oversee the architecture and infrastructure required to build and deploy AI technologies that can adapt and evolve based on various data inputs and user interactions. expertise in AI algorithms, machine learning, and neural networks will be essential in creating intelligent systems that can solve complex problems.

Key Responsibilities:

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 complex algorithms. Algorithm Development: Develop and implement advanced AI algorithms and models, including machine learning, deep learning, and neural networks. Continuously evaluate and improve algorithms to enhance system performance and 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 state-of-the-art 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 seamless communication and compatibility between AI components and other software modules. Ethical and Responsible AI: Ensure adherence 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 new approaches and techniques that can enhance AI capabilities. Collaboration and Communication: Collaborate with cross-functional teams, including data scientists, software engineers, and business stakeholders, to define AI requirements and deliver AI solutions that meet business objectives. Communicate complex AI concepts and solutions effectively to both technical and non-technical stakeholders.
Proven 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.
Solid 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.