.. the cloud environment • Strong practical Linux or Windows-based systems Administration skills, with good knowledge on Storage, Security and network tracks • .. assessment of source architecture and map it to relevant target architecture in the cloud environment • Strong practical Linux or Windows-based systems .. read more
Experience - 10 to 15 yrs
Location - Bangalore / Pune
Domain - VLSI / Connectivity / Embedded / I4.0 / Automotive
Qualifications:
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.