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

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 methodologies and practical AI quality assurance is beneficial. • Contributions to open-source AI projects or independently developed AI applications are advantageous. • Experience working in fast-paced, AI-focused technology environments is preferred. • Strong attention to detail and commitment to software reliability and maintainability. • Ability to manage technical priorities and deliver high-quality features in a remote environment. Engagement Details • Engagement Type: Full-time • Work Arrangement: Fully remote • Compensation: $220,000-$300,000/year • 1 available opening • Professional Focus: Applied AI engineering, LLM applications, AI agent development, and internal platform infrastructure. • Core Technologies: Python, LLM APIs, FastAPI, LangChain, LangGraph, and related AI development tools. • AI Systems Focus: Retrieval-augmented generation, prompt engineering, tool calling, and agent orchestration. • Infrastructure Focus: Cloud deployment and scalable backend services using AWS, GCP, Azure, or similar platforms. • Product Focus: Development and continuous improvement of an AI-powered recruiting agent. • Responsibilities will include building AI features, developing backend services, experimenting with model workflows, and evaluating system performance. • Assignments may involve API integration, RAG implementation, agent workflow development, cloud infrastructure, and production optimisation. • Deliverables will include production-ready AI capabilities, reliable backend services, evaluated model workflows, and technical documentation. • Previous experience shipping AI applications or intelligent agents to production is particularly relevant. • Strong Python engineering expertise, applied AI knowledge, independent problem-solving, and effective collaboration are important for successful performance.