Associate AI Researcher

About Role

As Associate AI Research Engineer specializing in Large Language Models (LLM) at eSuccess AI Technologies, you will be at the forefront of cutting-edge AI research focused on language understanding and generation. 

You will play a pivotal role in advancing our LLM capabilities and contributing to innovative AI solutions that leverage the power of natural language processing. 

If you're passionate about LLMs, have a strong background in research, and are excited about pushing the boundaries of language technology 

Education Qualification 

B.Tech Comp Sc. / MTech Comp Sc. from a reputed Institute/University 

Technical Skills 

• Strong programming skills in languages such as Python, PyTorch, TensorFlow, or similar. 

• Knowledge of transformer-based architectures and deep learning for language processing. 

• Strong analytical and problem-solving skills. 

• Excellent communication and collaboration abilities. 

• Eagerness to explore and adapt to emerging language technology trends. 

• Good knowledge as a Machine Learning Engineer (LLMs, FMs, Model Training, Testing, etc.) 

• Understanding of data structures, data modeling and software architecture 

• Deep knowledge of math, probability, statistics and algorithms 

• Ability to write robust code in Python and Javascript 

• Familiarity with Jupyter, machine learning frameworks (like Keras or PyTorch) and libraries (like scikit-learn) 

• Understanding of Microsoft Azure will be a big plus 

• Knowledge of any low/no code platform will be a big plus 

Personal Traits 

• Strong verbal and written communication skills 

• Good attitude, aptitude and ability to learn new technologies quickly 

• Ability to work in a team 

• Outstanding analytical and problem-solving skills 

Performing Art 

Any of Musical Instrument / Drama and Theater / Public debate / Writing / Singing / Comedy for effective interdisciplinary collaboration 

• LLM Research: Conduct advanced research in the field of Large Language Models, exploring novel algorithms, architectures, and techniques to enhance language understanding and generation. 

• Model Development: Design, implement, and optimize Large Language Models, utilizing state-of-the-art techniques such as transformer-based architectures. 

• Natural Language Understanding: Develop models for natural language understanding tasks, including text classification, sentiment analysis, named entity recognition, and more. 

• Text Generation: Create models for text generation tasks, including text completion, language translation, text summarization, and conversational agents. 

• Data Analysis: Collaborate with data scientists to preprocess and analyze language data, extracting valuable insights and patterns to inform model development. 

• Experimentation and Evaluation: Plan and conduct experiments to evaluate LLMs' performance, fine-tuning models to achieve state-of-the-art results. Implement rigorous testing and validation methodologies. 

• Collaboration: Work closely with cross-functional teams, including data scientists, machine learning engineers, and software developers, to integrate LLM solutions into real-world applications. 

• Documentation: Maintain clear and organized documentation of research methodologies, code, and results for internal knowledge sharing.