Remote | AI/ML Engineer, Internal Platforms — $220,000–$300,000/year

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 LangChain, LangGraph, or LlamaIndex.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 LangChain, LangGraph, LlamaIndex, 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 performance, reliability, and output quality.Strong understanding of prompt engineering and iterative AI application development.Demonstrated ability to collaborate effectively with engineering and product teams.Excellent written and verbal communication skills.Strong independent problem-solving skills and an experimental approach to technical challenges.Experience delivering production-ready AI applications or AI agents is strongly preferred.Familiarity with model evaluation metho