AI Engineer

SmartChat solution that has transformed live chat from a customer support cost to a conversion

rate optimisation tool for over 500 global brands, supercharging their website performance in

the process.

In ten years, we have grown from a founder-led business to a team of over 150+ employees

spanning four countries.

However, we are still at the tip of the iceberg. So, if you re looking to join a fast-paced ambitious company with a customer centric mindset, then look no further.

Build and enhance LLM-powered applications, AI agents and RAG pipelines.Design AI workflows involving LLMs, tools, APIs, retrieval and multi-step workflows.

Work with LLMs such as OpenAI, Claude and Gemini.

Build and improve conversational AI capabilities including summarization, NER, message generation and automated responses.

Work with vector databases, embeddings and retrieval pipelines to build knowledge- grounded AI applications.

Develop POCs and experiments to evaluate new models, frameworks and approaches.

Implement AI evaluation, testing and guardrails to improve the reliability and quality of AI applications. Work with AI observability to monitor traces, latency, token usage, cost and application

performance.

Integrate AI agents, tool calling and external APIs/services into AI applications. Understand and experiment with emerging AI approaches such as multi-agent systems

and agentic architectures.

Deploy AI applications to Dev and Pre-Prod environments, working closely with the

DevOps team.

Investigate and improve AI application quality, latency, reliability and cost.

Collaborate with engineering, data, product, and other teams to turn business

requirements into practical AI solutions.

What We're Looking For:

Must Have

3+ years of software/AI engineering experience, with practical experience building

AI/GenAI applications

Strong Python programming and backend/API development skills.

Hands-on experience building LLM-powered applications.

Strong understanding of RAG, embeddings, vector databases, and vector search/retrieval.

Practical experience with AI agents, tool calling, and agentic workflows.

Hands-on experience with at least one AI/LLM orchestration framework such as LangGraph, LangChain, LlamaIndex, or equivalent.

Understanding of AI evaluation, including designing test cases and evaluating the quality and reliability of LLM outputs.

Understanding of AI observability concepts, including traces, latency, token usage, cost, and model/application performance.

Understanding of guardrails, hallucination mitigation, and reliable AI application design.

Strong understanding of prompt engineering, including system prompts, few-shot

prompting, structured outputs, context management, and iterative prompt optimization.

Strong software engineering, debugging, and problem-solving skills.

Experience writing unit tests and working with production-quality code.

Basic understanding of Docker and application deployment.

Ability to work with APIs, third-party services, and external tools.

Good to Have

Hands-on experience with AI observability platforms such as Langfuse, LangSmith, or equivalent. …