AI Engineer

AI Engineer (Remote)

Location: Remote
Employment Type: Full-Time
Start Date: ASAP
Salary: Market-related (depending on experience)

About the Role

We are seeking a skilled AI Engineer to design, build, and deploy machine learning models into production environments. This role sits at the intersection of data science and software engineering, ensuring that AI solutions are scalable, reliable, and deliver real business value.

You will work closely with data scientists, engineers, and product teams to take models from concept to production, optimizing performance and integrating them into real-world applications.

Key Responsibilities

• Design, develop, and deploy machine learning and AI models into production
• Collaborate with data scientists to operationalise models and algorithms
• Build scalable APIs and services to serve AI models
• Optimise model performance, latency, and scalability
• Implement monitoring, logging, and versioning for deployed models
• Work with large datasets for training and validation
• Ensure best practices in software engineering, testing, and CI/CD
• Troubleshoot and improve existing AI systems

Technical Requirements

• Strong proficiency in Python
• Hands-on experience with TensorFlow, PyTorch, or similar frameworks
• Experience deploying ML models into production environments
• Knowledge of MLOps practices (CI/CD, model versioning, monitoring)
• Experience with REST APIs, microservices, or backend systems
• Familiarity with cloud platforms (AWS, Azure, or GCP)
• Experience with Docker/Kubernetes is advantageous
• Solid understanding of software engineering principles

Preferred Experience

• Experience working with large-scale data pipelines
• Exposure to LLMs, NLP, or computer vision projects
• Understanding of data engineering workflows
• Experience with model optimisation and performance tuning

Key Competencies

• Strong problem-solving and analytical thinking
• Ability to bridge technical and business requirements
• Excellent collaboration and communication skills
• Self-driven and comfortable working in a remote environment

What Success Looks Like

• AI models successfully deployed and integrated into production systems
• Improved system performance and scalability
• Seamless collaboration between data science and engineering teams
• Reliable, maintainable, and monitored AI solutions