Job Title

Required Skills - Proficient in multiple machine learning (ML) and deep learning (DL) frameworks such as TensorFlow and PyTorch, along with expertise in programming languages like Python, R, SQL, Scala, and Julia. - Demonstrated proficiency in deploying Large Language Models (LLM) and Generative AI applications, with knowledge of techniques like Prompt Engineering Fine-Tuning (PEFT) and Retrieval-Augmented Generation (RAG). - Experience in developing and deploying deep neural networks and ML models for intricate tasks. - Develop and construct platforms and tools tailored for Responsible AI principles such as fairness, security, and explainability across various model types (ML/DL/LLMs), data formats, and lifecycle stages. - Implement solutions and methodologies within existing AI projects to serve as safeguards against diverse model vulnerabilities, including toxicity and adversarial attacks. - Collaborate with business analysts, engineers, and stakeholders to ensure alignment of data science initiatives with Responsible AI principles. - Assist in devising and executing rigorous adversarial testing protocols for AI models to uphold Responsible AI standards. - Explore cutting-edge techniques, architectures, and methodologies to automate adherence to Responsible AI practices throughout the AI lifecycle, spanning from data preparation to model deployment and inferencing. - Establish monitoring systems to track model performance over time and institute mechanisms for regular model updates and maintenance. - Share insights and expertise across the organization to foster thought leadership and innovative strategies for addressing various aspects of Responsible AI. - Knowledge of architectural design patterns, performance tuning, database, and functional designs. - Hands-on experience in Service Oriented Architecture. - Ability to lead solution development and delivery for the design solutions. Qualification & Technical Skills - Bachelor’s or master’s degree in computer science, Artificial Intelligence, or a related field. - Experience working as an AI Technical Lead or Architect, working knowledge of machine learning, deep learning, and natural language processing (NLP) techniques. - Proficiency in programming languages such as Python, Java, or C++, and familiarity with popular AI libraries and frameworks (e.G., TensorFlow, PyTorch, Keras). - Experience in designing and implementing large-scale AI solutions, including data ingestion, storage, processing, and deployment. - Good understanding of cloud computing platforms (e.G., AWS, Azure, Google Cloud) and experience deploying AI models on these platforms. - Excellent problem-solving and analytical skills, with the ability to break down complex problems into actionable components. - Strong communication and teamwork skills, with the ability to work effectively within multi-functional teams. - Ability to stay updated with the latest advancements in AI technologies, frameworks, and platforms. - Knowledge of ethical considerations and responsible AI practices is a plus. Key Responsibilities - Collaborate with Business / Practice Units, Relevant Stakeholders and Customers to understand business goals and determine AI requirements. - Design and develop AI architectures, frameworks, and algorithms that can support large-scale and sophisticated AI solutions. - Evaluate and select appropriate AI technologies, tools, and frameworks to achieve desired performance, accuracy, and scalability. - Own the development and implementation of AI models, ensuring consistency to standard processes in machine learning and deep learning. - Develop and maintain AI pipelines, incorporating data cleaning, pre-processing, feature engineering, model training, and validation processes. - Conduct regular code reviews and provide technical guidance to junior members of the team. - Stay up to date with the latest advancements in AI technologies, frameworks, and algorithms, and find opportunities for their application in the organization. - Collaborate with infrastructure teams to ensure smooth deployment and monitoring of AI models in production environments. - Document AI architectures, design decisions, and technical specifications for reference and knowledge sharing.