Gen AI /Agentic AI

Deep understanding of Generative AI concepts: This includes techniques such as Generative Adversarial Networks (GANS), Variational Autoencoders (VAES), and other models used for data generation.

Proficiency in programming languages: This includes proficiency in Python, along with AI libraries and frameworks like TensorFlow, PyTorch, or Keras. Experience with cloud platforms: This includes experience with cloud platforms, such as AWS or Azure, and knowledge of containerization and orchestration tools like Kubernetes.

Strong understanding of machine learning and deep learning methodologies: This includes deep generative models, autoregressive models, and reinforcement learning for generative tasks.

Domain knowledge and problem-solving skills: The role requires the ability to understand specific industry domains and apply AI solutions to address real-world challenges. Excellent communication and collaboration skills: This is necessary to effectively communicate complex AI concepts to both technical and non-technica Enterprise GenAI Architecture & Strategy: The role defines and evolves the architecture for Generative AI systems within an organization. This includes creating scalable and secure platforms for various business domains and use cases.

audiences.

LLM Model Development & Deployment: The role leads the selection, fine-tuning, and deployment of large language models (LLMS) to ensure optimal performance, precision, and scalability. This involves working with various LLM models (like GPT, Claude, Gemini, or Llama) and related frameworks (e.g., LangChain and Llama Index).

Data Pipeline & MLOps: The role designs and implements data and ML pipelines, including data cleansing, pre-processing, model training or fine-tuning and feedback loops. It also involves designing and managing CI/CD pipelines in the Generative AI space, including LLMOPS.

Prompt Engineering & Model Optimization: The role develops advanced architectural patterns for prompt engineering and implements intent analysis techniques to enhance AI agent decision-making and user interactions. This also includes optimizing models for performance and efficiency. Cross-Functional Collaboration: The role collaborates closely with business teams, data scientists, software engineers, and other stakeholders to understand business needs and translate them into effective Generative AI architectures. This also involves promoting best practices through knowledge sharin and training.

Security & Responsible AI: The role implements security measures and ensures compliance with ethical considerations and responsible AI practices. Thi involves assessing and mitigating risks and promoting transparency and explainability. Innovation & Thought Leadership: The role stays up-to-date on the latest trends and advancements in Generative AI technologies and identifies opportunities for their use within the organization. This also includes serving as a thought leader and influencing both technical strategies and executive- level decisions.