Machine Learning Engineer - Early Career (UK)

Jobright is your personal AI job search agent that transforms the way you do job search from solo, time-consuming efforts to a fast, expert-guided journey, simplifying every job search step and accelerating your route to the best job outcomes. The Machine Learning Engineer will be responsible for designing and maintaining infrastructure for deploying AI agents, optimizing LLM pipelines, and developing automated systems for model reliability. Why Join Us • Build real, production AI agents used by real users • High ownership and impact • Work at the intersection of AI, agents, and product • Shape how people experience AI-driven job search Responsibilities • Design, build, and maintain the scalable infrastructure required to deploy and serve production-grade AI agents • Implement and optimize Large Language Model (LLM) pipelines, focusing on latency reduction, throughput, and efficient resource utilization • Develop automated systems for model monitoring, testing, and continuous integration to ensure the reliability of our AI agents • Optimize data ingestion and processing layers to support real-time agent responsiveness and complex RAG (Retrieval-Augmented Generation) architectures • Architect and refine APIs and backend services that bridge the gap between AI models and the user-facing product Qualification Required • Recent graduate or early-career professional (0–2 years of experience) with a degree in Computer Science, Software Engineering, or a related technical field • Strong proficiency in Python and experience with backend frameworks (such as FastAPI, Flask, or Django) • Practical experience with machine learning frameworks (PyTorch or TensorFlow) and a solid understanding of software engineering best practices (version control, CI/CD, unit testing) • Familiarity with the deployment of LLMs and an understanding of the infrastructure required to support autonomous agents Preferred • Previous internship or project experience in ML Ops, backend engineering, or distributed systems within an AI-focused company • Hands-on experience with containerization (Docker, Kubernetes) and cloud infrastructure (AWS, GCP, or Azure) • Knowledge of vector databases (such as Pinecone, Milvus, or Weaviate) and their role in production AI systems • Strong foundation in SQL and NoSQL database management for high-scale data handling