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# Machine Learning Engineer

- **Company:** Hirivo
- **Location:** London, UK (Remote available)
- **Salary:** £35,000.0 - £45,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, C++ (Programming Language), Cloud Computing, Nvidia CUDA, Continuous Integration, Web Scraping, Monitoring of Systems, Python (Programming Language), Machine Learning, Node.Js, Performance Tuning, Recommender Systems, OpenAI, Software Safety, TypeScript, Datadog, Pytorch, Retrieval-Augmented Generation, Large Language Models, Backend, LangSmith, Kubernetes, Information Technology, Free and Open-Source Software, Machine Learning Operations, Drift Detection, Evaluation of Large Language Models, Restful APIs, Data Pipelines, Docker, Golang
- **Published:** October 18, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=d63361d10f42e413

## About the Role

* Strong Python and solid software-engineering fundamentals (APIs, backend services, testing). (Python appears in 64% of the sampled postings; APIs/backend in 54%.)
 * Hands-on experience building with LLMs: OpenAI/Anthropic APIs, prompting, agents or RAG. (LLMs 60%, agents 69%.)
 * Experience shipping ML or AI systems to production and running them at scale. (Production 63%, scale 67%.)
 * Experience training and evaluating models, plus familiarity with PyTorch. (Training/experimentation 48%.)
 * Cloud experience (AWS, GCP or Azure) plus Docker/Kubernetes. (Cloud 40%.)
 * A degree in Computer Science or a related field, or equivalent experience.
 * A product mindset: you care about users and measurable impact. (36%.)

Desirable

 * Building LLM evaluation frameworks (evals, benchmarks, LLM-as-judge). (40%)
 * Monitoring and observability tooling (e.g. Datadog, OpenTelemetry, LangSmith). (55%)
 * Open-source contributions or a public portfolio of AI projects. (36%)
 * A second language such as TypeScript, Go, C++ or Rust. (TypeScript/JS 31%, Go 16%)
 * GPU / CUDA / inference-performance optimisation. (22%)
 * Fine-tuning (LoRA, RLHF/DPO). (18%)
 * Familiarity with AI safety or responsible AI. (21%)
 * Recommender/ranking systems, MLOps tooling (MLflow, model registry), web scraping/data pipelines. (13-15% each)

## Description

* Create evaluation sets and automated evals for form-fill accuracy, match quality and agent success rate, then iterate on prompts, models and fine-tunes.
 * Train, fine-tune and benchmark models (PyTorch), for example to parse CVs and job descriptions and to classify job fit.
 * Deploy models and agents as scalable APIs and services on the cloud (AWS/GCP) with Docker, Kubernetes and CI/CD, integrated with our Node backend.
 * Add observability: tracing of agent runs, latency/cost dashboards, failure and drift monitoring.
 * Build data pipelines that collect and clean job listings from many sources.
 * Work with product and with candidates to turn real job-seeker problems into shipped features, and care about responsible AI: candidates' data, fairness and accuracy.