AI Engineer

Roles and responsibilities

An AI Engineer is a specialized role focused on developing and implementing artificial intelligence (AI) systems, algorithms, and models that enable machines to mimic human behavior, solve problems, and make decisions. AI Engineers typically work in fields like machine learning, deep learning, natural language processing (NLP), computer vision, robotics, and more. This role requires a combination of software engineering, data science, and domain-specific expertise in AI technologies.

Here’s a breakdown of the essential skills and responsibilities for an AI Engineer:

1. Strong Programming Skills

• Python: Python is the primary language for AI development due to its simplicity and extensive library support. Common libraries include:
  • TensorFlow, Keras, PyTorch: Popular deep learning frameworks.
  • NumPy, Pandas, Matplotlib: For data manipulation, analysis, and visualization.
  • Scikit-learn: For traditional machine learning algorithms.
• C++: For performance-intensive applications, such as robotics or real-time AI systems.
• Java and R: Sometimes used for AI, especially in enterprise applications or data science roles.

2. Machine Learning & Deep Learning

• Supervised & Unsupervised Learning: Knowledge of algorithms for regression, classification, clustering, and dimensionality reduction.
  • Algorithms: Linear regression, decision trees, random forests, k-means clustering, etc.
• Neural Networks: Understanding the architecture and working of neural networks, including feedforward, convolutional (CNNs), and recurrent (RNNs) networks.
• Deep Learning: Hands-on experience with deep learning models for tasks like image classification, speech recognition, and NLP.
  • Libraries like TensorFlow and PyTorch are essential for building, training, and optimizing deep learning models.
• Reinforcement Learning: If you're working in areas like robotics or autonomous systems, knowledge of reinforcement learning (RL) is important.

3. Data Science & Statistical Analysis

• Data Preprocessing: Cleansing, transforming, and preparing data for machine learning models.
• Feature Engineering: Creating new features from raw data to improve model performance.
• Exploratory Data Analysis (EDA): Using statistical methods and visualizations to understand the dataset and derive insights.
• Statistical Modeling: Familiarity with hypothesis testing, confidence intervals, p-values, and distributions.

4. Natural Language Processing (NLP)

• Text Data Processing: Understanding how to preprocess and manipulate text data using libraries like spaCy, NLTK, and transformers.
• NLP Models: Building and deploying models like BERT, GPT, Word2Vec, or T5 for tasks like text classification, sentiment analysis, machine translation, and question answering.

5. Computer Vision

• Image Processing: Techniques like edge detection, object detection, image segmentation, and feature extraction.
• Convolutional Neural Networks (CNNs): Building and training CNNs for image classification, facial recognition, and object detection.
• Libraries & Tools: Familiarity with tools like OpenCV, TensorFlow, Keras, and PyTorch for implementing computer vision tasks.

Desired candidate profile

The AI Engineer will be the AI specialist, not only inhouse but from the client side as well. The team is very impressive and supportive in terms of guiding and mentoring as you grow within the organisation.

To be considered for this role, the successful candidate should worked in a similar role previously and hold a Bachelor’s or Masters’s degree in Computer Science, Artificial Intelligence or a related field.

Experience with Tools such as Microsoft Copilot, Copilot Studio, Power Automate, Synthesia AI, Otter AI, Fireflies AI, is essential for this role. Our client is looking for individuals proficient in Python and familiar with frameworks like TensorFlow, PyTorch, or Hugging Face. The successful candidate should also have knowledge of AI Agents and their implementation. Excellent communication skills in English are essential for this position.