Job Description :

What will you do?

- Implement technology solutions using Large Language Models (LLMs) like GPT

- Fine tune LLMs with proprietary data

- Use LLMs in conjunction with other domain specific models like Vision, NLP, Speech recognition, Image generation

- Setup integrations with hosted LLMs using various cloud providers (AWS+HuggingFace, Azure+OpenAI, Google+Bard)

- Setup and integrate hosted LLM models.

- Enhance the hosted LLM models with proprietary data.

- Write APIs using Python/Go to enhance and Integrate with existing API infrastructure.

- Design Prompts that provide appropriate responses.

- Use various prompt engineering methods (introspection, chain-of-thought, instruction prompting).

- Work with data teams to collect and organize the data needed for the task at hand.

- Ensure that best practices are followed in solving AI problems including thorough problem analysis, prior research understanding, persistent experimentation and documentation, rapid prototyping and extensive testing.

- Help in filing for patents and publishing research papers at AI conferences.

- Extensively document learnings from experiments and share with ML engineering team

- Use Copilot/CodeWhisperer in developing code and create "cookbook" for good code generation.

What you need to have?

- Hands-on industry experience of 1 to 2 years working on machine learning and deep learning or related fields.

- Sound understanding of Deep Learning in at least two AI problem domains, preferably, Computer Vision, NLP/NLG using LLM, Time Series Forecasting.

- Having a strong mathematical background in linear algebra, probability, and calculus.

- Familiar with machine learning frameworks such as PyTorch or TensorFlow, Keras.

- Proficient in Python based programming

- Understanding of modeling a problem into a Deep Learning framework.

- Exposure in building, measuring and iterating on neural network architectures that effectively solved the problem

- Experience working in a product-based startup environment

- Understanding of pipelines to monitor, extract, index, build and tune ML and NLP models

- Experience working in collaborative software development environments including the use of git, peer code review and independent authorship of well tested, maintainable and documented code.

- Data Management

- Defining validation strategies

- Defining the pre-processing or feature engineering to be done on a given dataset

- Defining data augmentation pipelines

- Exploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world

- Working proficiency with SQL and relational databases, data warehouse.

- Experience with GPU/CUDA for computational efficiency.

- Experience with ML Ops frameworks like Sagemaker/AWS, MLFlow or similar

- Familiar with distributed computational frameworks (YARN, Spark, Hadoop)