Machine Learning/Deep Learning Engineer With AI

Overview

On Site
$60,000 - $80,000
Full Time

Skills

ML
Deep Learning
AI
Python Libraries
Java & C++
R
DSA
ML Algorithms
Data Handling & Analysis
NLP
Big Data
Model Deployment
SQL/NoSQL
AWS & GCP
CI/CD
Business Acumen

Job Details

Technical Skills:
  • Programming Languages:
    Proficiency in languages like Python (including libraries like NumPy, Pandas, Scikit-learn, TensorFlow, and PyTorch), R, Java, or C++ is essential for coding, data manipulation, and model development.
  • Mathematics and Statistics:
    A strong foundation in mathematics (calculus, linear algebra) and statistics (probability, distributions, hypothesis testing) is crucial for understanding the underlying principles of machine learning algorithms.
  • Machine Learning Algorithms:
    Knowledge of various algorithms, including supervised (linear regression, logistic regression, SVM, decision trees), unsupervised (clustering, dimensionality reduction), and reinforcement learning, is fundamental.
  • Data Handling and Analysis:
    Skills in data collection, cleaning, preprocessing, and exploratory data analysis are vital for preparing data for training models.
  • Deep Learning:
    Understanding neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and related concepts is essential for handling complex data and tasks.
  • Natural Language Processing (NLP):
    Skills in NLP are important for tasks like text analysis, sentiment analysis, machine translation, and chatbot development.
  • Big Data Technologies:
    Knowledge of tools and technologies for handling large datasets, such as Hadoop, Spark, and cloud computing platforms (AWS, Azure, Google Cloud), is beneficial.
  • Model Deployment:
    The ability to deploy trained models into production environments and monitor their performance is a key skill.
  • Database Management:
    Familiarity with databases (SQL, NoSQL) and data warehousing is important for storing and retrieving data used in machine learning.
Non-Technical Skills:
  • Problem-Solving:
    The ability to identify problems, analyze them, and develop effective solutions using machine learning is critical.
  • Communication:
    Clearly communicating findings, results, and project progress to technical and non-technical audiences is essential.
  • Teamwork and Collaboration:
    Machine learning projects often involve working in teams, so the ability to collaborate effectively is important.
  • Continuous Learning:
    The field of machine learning is constantly evolving, so a willingness to learn new technologies and methodologies is necessary.
  • Business Acumen:
    Understanding the business context and how machine learning can be applied to solve real-world proble
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