About the job
REQUIREMENTS:
Total experience of over 8 years in the field.
Extensive knowledge of large language models (LLMs) such as GPTs, Llama, Claude, Gemini, Qwen, Mistral, and BERT-family architectures with a focus on Transformers.
Demonstrated expertise in prompt engineering with hands-on experience in implementing retrieval-augmented generation (RAG) patterns.
Proficient in Python with a solid grasp of AI/ML libraries including LangChain, LlamaIndex, LangGraph, LangSmith, Hugging Face Transformers, Scikit-learn, and PyTorch/TensorFlow.
Adept at implementing RAG architectures and fine-tuning models, including evaluation techniques.
Experienced with managed AI/ML services on cloud platforms such as Azure Machine Learning Studio and AI Foundry.
Strong understanding of vector databases like Weaviate and Neo4j.
Familiar with Generative AI evaluation metrics, including BLEU, ROUGE, perplexity, semantic similarity, and human evaluation.
Proven success in deploying AI/ML solutions into production environments.
Knowledgeable in MLOps pipelines for automation and monitoring.
Experience in developing generative AI applications.
Exceptional communication skills with the ability to collaborate across diverse teams.
RESPONSIBILITIES:
Analyze client business use cases and technical requirements, transforming them into elegant technical designs.
Align decisions with client requirements and effectively communicate these to developers.
Identify and evaluate various solutions to select the best fit for client needs.
Establish guidelines and benchmarks for non-functional requirements during project implementation.
Create and review design documentation that details overall architecture, framework, and high-level application design for developers.
Assess architecture and design across aspects such as extensibility, scalability, security, design patterns, user experience, and non-functional requirements, ensuring adherence to best practices.
Design and develop comprehensive solutions addressing both functional and non-functional requirements, defining technologies and frameworks necessary for execution.
Analyze technology integration scenarios and apply findings to projects.
Resolve issues arising during code reviews through thorough root cause analysis, justifying decisions made.
Conduct proof of concepts to validate that proposed designs and technologies meet project requirements.