Senior Deep Learning Engineer

•

Master’s degree or PhD in Computer Science, Artificial Intelligence, or Applied Mathematics with 5+ years of experience

•

Excellent programming skills in Python with strong fundamentals optimizations and software design

•

Solid understanding of ML/DL techniques, algorithms and tools with exposure to CNN, RNN, Transformers (GPT, Megatron, LLMs)

•

Hands-on experience with conversational AI technologies

•

Experience with training and fine-tuning LLMs for different dialog system tasks using PyTorch Deep Learning frameworks and performing data wrangling and tokenization

•

Solid understanding of agile development life cycle and experience with developing workflows and traceability and versioning of datasets including knowhow of database management and queries (SQL, DB)

•

Strong collaborative and interpersonal skills, and optimally guide and influence within a dynamic matrix environment

Responsibilities

•

You will have an opportunity to develop algorithms and apply your ideas at scale impacting Aerospace Engineering products

•

You will develop high-impact and high-visibility mode

•

Develop, train, fine-tune, and deploy models for driving conversational AI systems including LLMs, multimodal understanding, fuzzy search, image recognition, UI and UX, dialog reasoning and speech synthesis

•

Build novel data-driven paradigms for conversational AI including customized checklists for different engineering design practices and use-cases

•

Develop systems and frameworks using various data modalities (images, drawings, text, documents, checklists, etc.) and the roles they play in different levels of reasoning and decision making

•

Apply alignment techniques such as instruction tuning and reinforcement learning from human feedback to improve use-cases

•

Measure and benchmark model and application performance and analyze model accuracy and bias and recommend the next course of action and improvements

•

Drive the gathering, building, and annotation of domain specific datasets to train models for different design practices and applications and maintain model evaluation systems and characterize performance and quality metrics across platforms for various AI systems