Deep understanding of Generative AI fundamentals and transformer-based architectures.
Deep understanding of Natural Language Processing (NLP) and experience with models like GPT, BERT, T5, etc.
Strong working experience in Python and key ML libraries such as TensorFlow, PyTorch, and Hugging Face Transformers.
Hands-on experience with prompt engineering, fine-tuning, and retrieval-augmented generation (RAG) techniques.
Experience deploying models on cloud platforms such as AWS, GCP, or Azure, and knowledge of MLOps best practices.
Collaborate with product managers, data scientists, and engineering teams to deliver AI-powered solutions.
Familiarity with containerization (Docker), orchestration (Kubernetes), and CI/CD for ML pipelines.
Strong analytical, problem-solving, and communication skills.
RESPONSIBILITIES:
Understanding the client's 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.
