Kloud9 - Machine Learning Engineer (5-10 yrs)

Job Description :

Responsibilities :

- Studying, transforming, and converting data science prototypes

- Deploying models to production

- Training and retraining models as needed

- Analyzing the ML algorithms that could be used to solve a given problem and ranking them by their respective scores

- Analyzing the errors of the model and designing strategies to overcome them

- Identifying differences in data distribution that could affect model performance in real-world situations

- Performing statistical analysis and using results to improve models

- Supervising the data acquisition process if more data is needed

- Defining data augmentation pipelines

- Defining the pre-processing or feature engineering to be done on a given dataset

- To extend and enrich existing ML frameworks and libraries

- Understanding when the findings can be applied to business decisions

- Documenting machine learning processes

Basic requirements :

- 6+ years of IT experience in which at least 3+ years of relevant experience primarily in converting data science prototypes and deploying models to production

- Proficiency with Python and machine learning libraries such as pandas, xgboost

- Strong working experience with pyspark

- Experience with Machine Learning life cycle and training/retraining

- Strong expertise in using kubeflow/airflow and docker containerization

- Knowledge of Big Data frameworks like Hadoop, Spark, etc

- Experience in working with ML frameworks like TensorFlow, Keras, OpenCV

- Strong written and verbal communications

- Excellent interpersonal and collaboration skills.

- Expertise in visualizing and manipulating big datasets

- Familiarity with Linux

- Ability to select hardware to run an ML model with the required latency

- Robust data modelling and data architecture skills.

- Advanced degree in Computer Science/Math/Statistics or a related discipline.

- Advanced Math and Statistics skills (linear algebra, calculus, Bayesian statistics, mean, median, variance, etc.)

Nice to have :

- Familiarity with Scala, Java, and R code writing.

- 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

- Verifying data quality, and/or ensuring it via data cleaning

- Supervising the data acquisition process if more data is needed

- Finding available datasets online that could be used for training