ML Engineers

SoHo Dragon is looking to hire an ML Engineer for a contract opportunity. 

Must have skills:

Basic experience with generative AI, LLM operations, AI assistant, prompt engineering, embeddings, AI background, machine learning.

Responsibilities:

•  Develop, Train, Finetune, and Deploy large language models for text completion and chats in different applications including coding, NLU, NLG, IRQA, machine translation, and dialog, reasoning, and tool systems.
•  Apply instruction tuning, reinforcement learning from human feedback (RLHF), and parameter efficient finetuning such as p-tuning, adaptors, LoRA, and so on to improve LLMs for different use cases.
• Measure and benchmark model and application performance
•  Analyze model accuracy and bias and recommend the next course of action & Improvements.
•  Maintain model evaluation systems.
•  Drive the gathering, building, and annotation of domain specific datasets to train LLMs for different tasks and applications.
•  Gather knowhow on datasets for LLM training & evaluation.
•  Characterize performance and quality metrics across platforms for various AI and system components.

Qualifications:

• Master’s degree (or equivalent experience) or PhD in Computer Science, Electrical Engineering, Artificial Intelligence, or Applied Math with 3 years of experience.
•  Excellent programming skills in Python with strong fundamentals in programming, optimizations and software design
•  Strong knowledge of ML/DL techniques, algorithms, and tools with exposure to CNN, RNN (LSTM), Transformers (BERT, BART, GPT/T5, Megatron, LLMs)
•  Hands-on experience on conversational AI Technologies like Natural Language Understanding, Natural Language Generation, Dialog systems (including system integration, state tracking and action prediction), Information retrieval and Question and Answering, Machine Translation etc.
•  Experience with Training BERT, GPT for different NLP and dialog system tasks using “PyTorch” Deep Learning Frameworks and performing NLP data wrangling and tokenization
• Understanding of MLOps life cycle and experience with MLOps workflows & traceability and versioning of datasets including knowhow of database management and queries (in SQL, MongoDB etc)
•  Knowledge of end-to-end MLOps platform such as Kubeflow, MLFlow, AirFlow