Job Description

Responsibilities:

• 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

Requirements:

• 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
• 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
• 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)