Machine Learning Engineer - Python/Deep Learning

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 (AWSHuggingFace, AzureOpenAI, GoogleBard) - 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) (ref:hirist.tech)

Location: Bangalore, IN

Posted Date: 4/24/2024