The newly formed Use Case Delivery Team will be tasked with building innovative GenAI applications. We will be building GenAI solutions end-to-end: from concept, through prototyping, productization, to operations.
The ideal candidate will bring technical expertise in Natural Language Processing (NLP), especially leveraging Large Language Models (LLM) and proficiency in prompt engineering techniques.
responsibilities :
Collaborate with ML & MLOps engineers, product owners, and other developers in Agile teams to integrate LLMs into scalable, robust, fair and ethical end-user applications, focusing on user experience, relevance, and real-time performance
Design, develop, customize, optimize, and fine-tune LLM-based and other AI-infused algorithms tailored to specific use cases such as text generation, summarization, information extraction, chatbots, AI agents, code generation, document analysis, sentiment analysis, data analysis, etc.
Design data pipelines to curate, preprocess, and structure datasets that improve LLM-based algorithms performance and reduce biases, with a focus on data quality and diversity
Develop and optimize prompts to guide LLM outputs, enhancing performance for specific tasks, working closely with subject matter experts, business analysts, prompt engineers and other stakeholders
Conduct rigorous experimentation, including A/B testing, to evaluate algorithm performance against benchmarks and control groups; use metrics specific to generative AI as well as pre-GenAI techniques, as required
Apply software development best practices, including writing unit tests, configuring CI/CD pipelines, containerizing applications, setting up cloud infrastructure and APIs; ensure robust logging, experiment tracking, and model monitoring
Implement techniques to identify and mitigate biases in LLM outputs, ensuring responsible and ethical AI deployment
requirements-expected :
3+ years working with advanced machine learning algorithms
3+ years of hands-on experience working with language models, especially those based on Transformer architectures (e.g. BERT, T5, RoBERTa), and at least 1 year of experience with generative large language models (e.g. GPT, LLaMA, Claude, Cohere, etc.)
Advanced proficiency in Python and experience with deep learning frameworks such as PyTorch or TensorFlow
Expertise with Transformers architectures, LangChain, or similar LLM ecosystems
Experience with designing end-to-end RAG systems using state of the art orchestration frameworks
Practical overview and experience with AWS services to design cloud solutions
Experience with working with GenAI specific services like Azure OpenAI, Amazon Bedrock, Amazon SageMaker JumpStart
