AI/ML Engineer

Role

As an AI/ML Engineer, you will work closely with the Founding AI/ML Tech Lead to research, design, develop and deploy AI-powered applications.

This is a hands-on role where you will have the opportunity to work across the full AI product lifecycle from research and experimentation to prototyping, development, testing and production deployment.

You will be encouraged to experiment with emerging AI technologies and contribute ideas that can become commercially valuable products and proprietary technology.

Key Responsibilities

AI/ML Development

• Design and develop AI/ML solutions for real-world enterprise use cases.
• Develop applications using Large Language Models (LLMs) and Generative AI.
• Build and optimise RAG-based applications, AI agents and intelligent workflows.
• Work with embeddings, vector databases, prompt engineering and model evaluation.
• Experiment with different AI models and approaches to improve accuracy, performance and cost.
• Develop AI pipelines for structured and unstructured data.
• Evaluate and benchmark AI models and solutions.

Product Development

• Convert business and technical requirements into working AI solutions.
• Build prototypes, PoCs and MVPs quickly.
• Develop reusable AI components and services.
• Contribute to taking successful prototypes into production.
• Build APIs and integrate AI capabilities into enterprise applications.
• Troubleshoot and optimise AI applications in production.

Data & Engineering

• Work with structured and unstructured datasets.
• Develop data preparation and processing pipelines.
• Work with relational and vector databases.
• Develop and consume REST APIs.
• Follow software engineering best practices, including Git, testing, documentation and code reviews.
• Support cloud deployment and monitoring of AI applications.

Research & Innovation

• Stay up to date with the latest developments in Generative AI, LLMs, AI agents and ML technologies.
• Evaluate new models, frameworks and tools for potential commercial use.
• Participate in internal research and experimentation.
• Identify opportunities to develop differentiated technology and intellectual property.
• Contribute to technical papers, prototypes, internal frameworks and proof-of-concept solutions where appropriate.

Team Collaboration

• Work closely with the AI/ML Tech Lead and software engineers.
• Participate in technical discussions and architecture reviews.

Document approaches, experiments and lessons learned.

Preferred candidate profile

Required Skills & Experience

Essential

• 1 to 3 years of experience in AI/ML, software engineering or a related technical discipline.
• Strong Python programming skills.
• Good understanding of machine learning and modern AI techniques.
• Hands-on experience with Generative AI and Large Language Models.

• Practical experience with one or more of:

• RAG
• LLM APIs
• Embeddings
• Vector databases
• Prompt engineering
• AI agents / agentic workflows
• NLP

• Model evaluation

• Experience working with APIs and databases.
• Familiarity with Git and software development practices.
• Experience with at least one cloud platform such as AWS, Azure or Google Cloud.
• Strong problem-solving and analytical skills.
• Ability to learn rapidly and work independently.

Desirable Skills

• Experience with LangChain, LangGraph or equivalent AI orchestration frameworks.
• Experience with OpenAI, Anthropic, Google Gemini or open-source LLMs.
• Experience with Hugging Face and open-source models.
• Experience with PyTorch or TensorFlow.
• Experience with Docker and CI/CD.
• Experience with document AI / intelligent document processing.
• Experience integrating AI with ERP or enterprise platforms such as SAP, Oracle, Microsoft Dynamics or Salesforce.
• Exposure to AI security, governance, evaluation or observability.
• Experience with model fine-tuning or parameter-efficient fine-tuning.

What We Are Looking For

We are particularly interested in candidates who are:

Curious genuinely interested in understanding how new AI technologies work.

Hands-on willing to build, experiment, test and learn rather than relying purely on theory.

Product-minded – interested in solving customer problems, not just building technology for its own sake.

Entrepreneurial – comfortable working in an early-stage environment where priorities can evolve quickly.

Analytical – abl…