Remote | AI​/ML Engineer, Internal Platforms

We are sharing a specialised full-time remote opportunity for an experienced AI/ML Engineer with strong expertise in Python software engineering, large language models, AI agents, backend API development, and cloud infrastructure to build and enhance advanced AI-powered internal platforms.

The selected professional will develop new capabilities for an AI recruiting agent, experiment with modern language models and agent frameworks, and deliver reliable, production-ready AI features. The role combines applied AI engineering with backend development, retrieval-augmented generation, agent orchestration, and continuous model evaluation to improve the accuracy, reliability, and practical effectiveness of intelligent recruiting workflows.

Key Responsibilities

LLM Application & AI Agent Development

• Design, develop, and enhance AI agent capabilities using modern large language models.
• Build intelligent workflows that support recruiting-related tasks and automated decision-support processes.
• Integrate LLM APIs and related AI services into internal applications.
• Experiment with different model configurations and implementation approaches to improve agent performance.
• Develop production-oriented AI features that deliver practical improvements to platform functionality.

Python Backend Engineering & API Development

• Build and maintain backend services using Python and modern application development frameworks.
• Design and implement APIs supporting AI-powered platform capabilities.
• Develop backend integrations connecting models, application services, and relevant data sources.
• Apply software engineering best practices to maintain reliability, scalability, and code quality.
• Collaborate with engineering teams to deliver maintainable backend infrastructure and new product features.

Prompt Engineering, RAG & Agent Orchestration

• Design and refine prompts, model workflows, and multi-step AI agent interactions.
• Develop retrieval-augmented generation (RAG) capabilities for relevant knowledge retrieval and contextual reasoning.
• Implement tool-calling and orchestration patterns for agent-based applications.
• Evaluate and improve agent workflows using frameworks such as Lang Chain, Lang Graph, or Llama Index.
• Identify opportunities to improve model context handling, task execution, and workflow reliability.

Model Evaluation & Performance Optimisation

• Evaluate the quality, accuracy, and reliability of AI-generated outputs.
• Design experiments to compare model behaviour, prompting strategies, and agent implementations.
• Identify weaknesses in AI system performance and implement targeted improvements.
• Refine evaluation methods and quality metrics to support consistent performance assessment.
• Apply experimental findings to improve the effectiveness and reliability of deployed AI features.

Cloud Deployment & Cross-Functional Product Delivery

• Deploy, maintain, and improve AI-powered services in cloud environments.
• Support scalable production operations using AWS, Google Cloud, Azure, or comparable infrastructure.
• Collaborate with product and engineering teams to translate requirements into practical AI solutions.
• Monitor emerging developments in LLMs, agent frameworks, and applied AI engineering tools.
• Maintain high standards of technical documentation, implementation quality, and cross-functional communication.

Ideal Profile

• Strong professional experience in Python software engineering and backend application development.
• Demonstrated experience building applications using large language models and AI APIs.
• Practical knowledge of LLM integration, prompting, and AI application architecture.
• Experience designing or implementing agent-based AI workflows.
• Familiarity with Lang Chain, Lang Graph, Llama Index
  , or comparable agent development frameworks.
• Experience building backend APIs using FastAPI or similar Python frameworks.
• Strong understanding of API design, service integration, and backend software engineering principles.
• Practical experience working with at least one major cloud platform, such as AWS, Google Cloud, or Azure
  .
• Understanding of scalable application architecture and production deployment practices.
• Familiarity with retrieval-augmented generation (RAG) systems and relevant retrieval workflows.
• Experience implementing tool calling, agent orchestration, or multi-step LLM applications is advantageous.
• Strong analytical reasoning and software debugging capabilities.
• Ability to experiment with different AI models and systematically evaluate results.
• Experience assessing model…