Bangalore, Karnataka, India Post Date: September 18, 2023 Full Time
Apply for job
Job Description
Responsibilities:
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.
Requirements:
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)