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

Must 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.

Nice to have

• 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)