Senior Artificial Intelligence Engineer
1 month ago
Job Description :
Designation : Senior AI Engineer.
Office Location: Bangalore (India).
Position description :
What will you do?.
- 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.
What you need to have :
Must Have :
- 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 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.
Nice to have :
- Experience in filing for patents and publishing research papers at AI conferences.
- 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).
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