Senior Machine Learning Engineer - Python

Company : Kloud9 : Bangalore Experience: 6-10 years Mode : Hybrid (2 days work from office in a week) About Us : Founded in 2016 and headquartered in New York, Kloud9 is one of the leading technology services and solutions providers specializing in Data Science, Artificial Intelligence (AI) and Machine Learning (ML). At Kloud9, we work closely with our customers across Retail, Consumer Goods, Healthcare & Manufacturing to achieve a satisfying customer experience using modern technologies in AI, ML, Cloud and Automation, that transform Data into action-driven insights. As a leading provider of cutting-edge cloud solutions, Kloud9 empowers businesses to harness the full potential of cloud technology, enabling them to scale, innovate, and thrive in the digital era. With a strong commitment to excellence and a customer-centric approach, Kloud9 is redefining the way organizations leverage the power of the cloud to drive growth and efficiency. Through our customer-centric approach and relentless commitment to excellence, we have helped numerous organizations achieve remarkable results in their cloud journey. Our success stories highlight the transformative impact of our cloud solutions, including cost savings, increased productivity, improved scalability, and enhanced business agility. With our strong technology expertise, we understand & visualize a new world with an AI-First Approach. Kloud9 is constantly on the lookout for top-tier talent to join us in this exciting Overview : As a Machine Learning (ML) Engineer at Kloud9, you will be responsible for designing machine learning systems, which involves assessing and organizing data, retraining models, performing statistical analysis to resolve data set problems, enhancing the accuracy of model prediction capabilities, and generally monitoring and optimizing machine learning processes to help develop strong performing machine learning 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 - 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 - 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 : - Understanding of ML Xgboost api and usage of dask cluster - 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 (ref:hirist.tech)

Location: bangalore, IN

Posted Date: 11/28/2024