REQUIREMENTS:
Total experience 13+ years.
Deep understanding of LLMs (e.g., GPTs, Llama, Claude, Gemini, Qwen, Mistral, BERT-family models) and their architectures (Transformers)
Should have expert-level prompt engineering skills and proven experience implementing RAG patterns
High proficiency in Python and standard AI/ML libraries (e.g., LangChain, LlamaIndex, LangGraph, LangSmith, Hugging Face Transformers, Scikit-learn, PyTorch/TensorFlow).
Experience implementing RAG architectures and prompt engineering.
Strong experience with fine-tuning and distillation techniques and evaluation.
Strong experience using managed AI/ML services on the target cloud platform (e.g., Azure Machine Learning Studio, AI Foundry).
Strong understanding of vector databases (e.g., Weaviate, Neo4j)
understanding of GenAI evaluation metrics (e.g., BLEU, ROUGE, perplexity, semantic similarity, human evaluation).
Proven track record in deploying AI/ML solutions into production environments.
Familiarity with MLOps pipelines for automation and monitoring.
Experience working on generative AI applications
Excellent communication skills and the ability to collaborate effectively with cross-functional teams.
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.