Kloud9 Technologies is Looking for ML Engineer Full Time

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: 

●       4+ years of IT experience in which at least 2+ years of relevant experience primarily in converting data science prototypes and deploying models to production

●       Proficiency with Python and machine learning libraries such as scikit-learn, matplotlib, seaborn and pandas

●       Knowledge of Big Data frameworks like Hadoop, Spark, Pig, Hive, Flume, 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 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