Deep understanding of Generative AI fundamentals and transformer-based architectures.
Strong experience in Cloud Architecture (e.g., AWS, Azure, GCP) for deploying scalable AI systems.
Proven experience with BERT, GPT, LLaMA, and similar LLMs.
Strong hands-on experience in prompt engineering and RAG techniques.
Experience in fine-tuning and deploying models using frameworks like Hugging Face Transformers, LangChain, or equivalent.
Familiarity with multi-agent AI systems and collaborative model workflows.
Proficient in Python and machine learning libraries (e.g., PyTorch, TensorFlow).
Strong problem-solving skills and a passion for AI innovation and ethical development.
Experience integrating models into enterprise platforms and APIs.
Understanding of ML Ops practices and CI/CD pipelines for AI deployment.
Background in Natural Language Processing (NLP) and Knowledge Engineering.
Excellent communication skills, with the ability to articulate solutions effectively.
RESPONSIBILITIES:
Understanding the clients business use cases and technical requirements and be able to convert them into technical design which elegantly meets the requirements.
Mapping decisions with requirements and be able to translate the same to developers.
Identifying different solutions and being able to narrow down the best option that meets the clients requirements.
Defining guidelines and benchmarks for NFR considerations during project implementation
Writing and reviewing design document explaining overall architecture, framework, and high-level design of the application for the developers
Reviewing architecture and design on various aspects like extensibility, scalability, security, design patterns, user experience, NFRs, etc., and ensure that all relevant best practices are followed.
Developing and designing the overall solution for defined functional and non-functional requirements; and defining technologies, patterns, and frameworks to materialize it
Understanding and relating technology integration scenarios and applying these learnings in projects
Resolving issues that are raised during code/review, through exhaustive systematic analysis of the root cause, and being able to justify the decision taken.
Carrying out POCs to make sure that suggested design/technologies meet the requirements.
