Senior Artificial Intelligence Engineer

1 month ago


Bangalore, Karnataka, India Pyxidia Techlab LLP Full time

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).

(ref:hirist.tech)
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