BriefCase
BriefCase

Machine Learning Engineer Job in Dataeaze

Machine Learning Engineer

Dataeaze 4+ weeks ago
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Job Summary


Qualifications

  • B Tech in Computer Science / Information Technology is required
  • M Tech / PhD specialization in artificial intelligence / machine learning is preferable
  • Prerequisites mentioned below
  • Work-experience is preferable, but we are looking for expertise rather experience in number of years

Roles and responsibilities

  • Code, train, evaluate and deploy machine learning models that integrate with the complete software solution.

Skills

  • Candidate must be good at programming and be able to adapt to any of the basic programming languages like C, C++, Python, Matlab, R, Julia, Java, Go, Rust etc.
  • Candidate must have mastery of basic computer science concepts like data structures, algorithms, databases, relational algebra (SQL), operating systems, computer architecture, computer networks.
  • Candidate must be comfortable in programming on GNU/Linux in a high performance computing (HPC) setups like multicores, clusters, GPUs, etc.
  • Candidate must be able to grasp concepts from latest research papers and implement them in a short time
  • Candidate must have a specialization in AI / ML and should have mastery over the topics in the prerequisites section
  • Candidate must be familiar with ML programming frameworks and libraries and should be able to quickly learn and adapt to the newly emerging ones

Prerequisites

Techniques

A) Undergraduate level machine learning:

  • Machine Learning | Stanford University CS229 | Prof. Andrew Ng | Editions: Original / Latest / Coursera OR
  • Elements of Statistical Learning: data mining, inference and prediction - Hastie, Tibshirani, Friedman | Introduction to Statistical Learning | EdX Course OR
  • Swayam | Introduction to Machine Learning | Prof. Balaraman Ravindran | IIT Madras
  • Machine Learning | Dr. Nando de Freitas OR
  • Machine Learning: A Probabilistic Perspective | Kevin Murphy OR equivalent

    • B) Introduction to deep learning

  • Dive into Deep Learning | Prof. Alex Smola OR
  • Practical deep learning for coders | Jeremy Howard OR
  • Deep Learning | Andrew Ng OR equivalent

    • It would be great, if you could provide a certificate of completions A (1) / (2) / (3) and B(3)

    Tools

    A machine learning engineer needs to be proficient in different aspects of computer science and engineering. Some of the tools to be familiar with include:

    • Data Management
      • Python, Julia, R, jupyter, Pandas, PySpark, numpy, matplotlib, seaborn, streamlit, Kafka
    • Core Computer Science
      • C, C++, Python,Java, Scala, NetworkX, igraph, MySQL, PostgreSQL, Linux, Mac, Windows
    • Machine Learning
      • PyTorch, Tensorflow, Keras, scikit-learn, XGBoost, LightGBM
    • Artificial Intelligence
      • OpenCV, dlib. scikit-image, nltk, SpaCy, faiss, flann, kaldi, sphinx, librosa
    • Systems / Computing
      • OpenMP, MPI, Spark, CUDA, AWS, GCloud, Azure, Mosquitto, Paho, Jetson Nano
    • Software Engineering
      • Docker, Git, JIRA, Trello, MLOps toolkits
    Experience Required :

    Fresher

    Vacancy :

    2 - 4 Hires

    Apply Now
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