AI Engineer

Roles and responsibilities

• Lead the development of AI products leveraging RAG, LLM, or NLP.|Create scalable, real-world AI solutions that drive business value.
  
  

About Our Client

A prestigious and growing government organisation.

Job Description

• Collaborate with cross-functional teams, including data scientists, product managers, and software engineers, to create and scale AI solutions.
• Manage the end-to-end lifecycle of AI products, from initial ideation and prototyping to production deployment and post-release improvements.
• Develop models and algorithms for a variety of applications, including text generation, semantic search, information retrieval, and more.
• Optimise models for performance, scalability, and robustness in real-world environments.
• Ensure that AI solutions are aligned with business goals, delivering value and enhancing customer experiences.
• Stay up-to-date with the latest advancements in AI, machine learning, and NLP to continuously improve the company's AI capabilities.
  
  

The Successful Applicant

• Bachelor's or Master's degree in Computer Science, AI, Machine Learning, or a related field.
• Proven experience with RAG, LLMs, and/or NLP technologies.
• Hands-on experience in building and deploying AI models from the ground up, taking projects from concept to production.
• Strong programming skills in Python, with experience using AI/ML libraries such as TensorFlow, PyTorch, Hugging Face, or similar frameworks.
• Experience with cloud platforms (preferably Azure) for AI model development and deployment.

Desired candidate profile

1. Model Development: Designing and building AI models using machine learning algorithms, deep learning techniques, and natural language processing.

2. Data Preparation: Collecting, cleaning, and preprocessing large datasets to ensure data quality and suitability for training AI models.

3. Algorithm Implementation: Implementing and optimizing algorithms for specific applications, such as image recognition, recommendation systems, or predictive analytics.

4. Performance Evaluation: Evaluating and fine-tuning AI models for accuracy, efficiency, and scalability, using metrics such as precision, recall, and F1 score.

5. Collaboration: Working with cross-functional teams, including data scientists, software engineers, and business stakeholders, to integrate AI solutions into existing systems.

6. Research and Innovation: Staying updated on the latest advancements in AI and machine learning technologies, and applying new techniques to improve models.

7. Deployment: Managing the deployment of AI models into production environments, ensuring they operate effectively and efficiently.

8. Monitoring and Maintenance: Continuously monitoring AI systems for performance issues and updating models as necessary to adapt to changing data or business needs.

9. Documentation: Creating and maintaining documentation for AI processes, models, and code to ensure clarity and reproducibility.

10. Ethics and Compliance: Ensuring that AI solutions comply with ethical standards and regulatory requirements, particularly regarding data privacy and bias.

Key Skills:

1. Programming Proficiency: Strong coding skills in languages such as Python, R, or Java, along with experience in AI frameworks (e.g., TensorFlow, PyTorch, Keras).

2. Mathematics and Statistics: Solid understanding of mathematical concepts, statistics, and algorithms used in machine learning and data analysis.

3. Machine Learning Expertise: Deep knowledge of machine learning techniques, including supervised and unsupervised learning, reinforcement learning, and deep learning.