Bangalore, Karnataka, India Post Date: September 17, 2023 Full Time
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Ensuring 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.
Building highly optimized AI training, validation, and data collection pipelines that work at a scale of 100s of millions of training examples.
Devising benchmark datasets and protocols for thorough performance evaluation of the solution against real-world test cases. Benchmarking against competing solutions, prior research, and customer acceptance criteria wherever applicable.
Productionizing the solutions and coming up with effective ways of deploying deep learning models.
Handling challenges w. r. t data requirements such as ideating the best ways of sourcing data, creating new tools for data annotation, etc. to effectively solve the AI problems.
Filing for patents and publishing research papers at AI conferences.
Building a structured training process for ML engineers in the team
Hands-on industry experience of 6 to 8 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.
Extensive experience with machine learning frameworks such as PyTorch or TensorFlow, Keras, being proficient in Python
Experience in machine learning and software engineering best practices with advanced skills in Python
Adept at modeling the problem into a Deep Learning framework. Prior exposure in building, measuring and iterating on neural network architectures that effectively solve the problem
Experience working with data teams to collect and organize the data needed for the task at hand.
Experience working in a product-based startup environment
Experience building and working with highly efficient distributed training approaches for deep learning models.
Be a results-oriented machine learning engineer.
Develop pipelines to monitor, extract, index, build and tune ML and NLP models
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
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.
Experience in filing for patents and publishing research papers at AI conferences.
Working proficiency with SQL and relational databases, and 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)