How mature is your AI / ML journey?

A guide for IT managers on hiring engineers and building an AI/ML team

Industry changing technologies have been coming steadily over the last couple 20 years: the Internet, E-commerce, Smart Phones, social media, Cloud based services, Robotics and IOT stand out. Each of these required new tech skills to maximize their potential for business.

Now, Generative AI is here, and it’s changing the landscape faster than any of the above. As an IT leader, it’s crucial to understand the different types of AI/ML engineers available, their technical skill sets, and the certifications that can enhance their expertise.

This article serves as a comprehensive guide to help IT managers navigate the hiring process, identify the right engineers, and build a successful AI/ML team. Only by fully understanding these roles can IT managers and recruiters get the right talent to successfully deploy new AI / ML applications.

1. Machine Learning Engineer:

Machine Learning Engineers specialize in developing and implementing machine learning algorithms. Their technical skill set focuses on:

  • Programming languages: Python, R, or Scala.
  • Machine learning frameworks: TensorFlow, PyTorch, scikit-learn.
  • Statistical concepts and algorithms.
  • Data preprocessing, feature engineering, and model training.


    • TensorFlow Developer Certificate.
    • AWS Certified Machine Learning – Specialty.
    • Microsoft Certified: Azure AI Engineer Associate.

2. Deep Learning Engineer

Deep Learning Engineers excel in designing and building neural networks for complex tasks. Their technical skill set includes:

  • Deep learning frameworks: TensorFlow, PyTorch, Keras.
  • Deep learning architectures: CNNs, RNNs, Transformers.
  • Training and fine-tuning pre-existing models.
  • GPU acceleration and distributed computing.


    • Deep Learning Specialization (Coursera).
    • NVIDIA Deep Learning Institute (DLI) Certifications.
    • Certified Deep Learning Engineer (CDLE) by AI Deep Learning.

3. Natural Language Processing (NLP) Engineer

NLP Engineers focus on building models for language processing tasks. Their technical skill set includes:

  • NLP libraries: NLTK, spaCy, Gensim.
  • Word embeddings and language models: Word2Vec, GloVe, BERT.
  • Sequence models: RNNs, Transformers.
  • Entity recognition, sentiment analysis, topic modeling.


    • Stanford University NLP Specialization (Coursera).
    • IBM Watson NLP Certification.
    • Certified NLP Engineer by AI Deep Learning.

4. Computer Vision Engineer

Computer Vision Engineers specialize in visual data analysis. Their technical skill set includes:

  • Computer vision frameworks: OpenCV, TensorFlow, PyTorch.
  • Convolutional Neural Networks (CNNs) and architectures.
  • Image preprocessing techniques.
  • Object detection, image classification, and segmentation.


    • NVIDIA Certified AI Specialist.
    • OpenCV AI Courses.
    • Certified Computer Vision Engineer by AI Deep Learning.

Computational techniques

Computational techniques are a set of mathematical and algorithmic methods used to solve complex problems and perform computations in various domains. In the context of AI/ML, computational techniques play a vital role in developing and training models, optimizing parameters, and making predictions. Here are some common computational techniques used in AI/ML applications:

  1. Genetic Algorithms: Genetic algorithms mimic natural selection to optimize models. They find applications in parameter tuning, feature selection, and neural network architecture optimization.
  2. Swarm Intelligence: Swarm intelligence models emulate collective behavior to solve complex problems. They are useful for optimization tasks, including clustering, routing, and scheduling.
  3. Reinforcement Learning: Reinforcement learning enables agents to learn from interactions with an environment. It is employed in robotics, game playing, and autonomous systems.
  4. Bayesian Networks: Bayesian networks model probabilistic relationships between variables. They are valuable for reasoning under uncertainty, decision-making, and risk analysis.
  5. Evolutionary Computing: Evolutionary computing encompasses techniques like genetic algorithms, genetic programming, and evolutionary strategies. It is used in optimization, design, and simulation problems.

Hiring the right AI/ML engineers is critical for creating new models successfully. Machine Learning Engineers, Deep Learning Engineers, NLP Engineers, and Computer Vision Engineers bring specialized skills and knowledge to the table. By understanding their skill sets and considering relevant certifications, IT managers can ensure they have a proficient team. Additionally, leveraging computational techniques can enhance the development process.

If you would like to engage with a recruiting firm that specializes in sourcing and qualifying these resources, your options are limited. Cirrus always focuses on new, cutting-edge technologies and we are ready to help you join this revolution.

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