{"id":2010637,"url":"https://alion.io/job/londonstockexchange-principal-machine-learning-engineer","title":"Principal Machine Learning Engineer","company":{"id":44904,"name":"London Stock Exchange","domain":"londonstockexchange.com","url":"https://alion.io/company/londonstockexchange","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Welcome to the Jungle","truth_index":{"grade":"A","score":100,"open_postings":16,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":59,"computed_at":"2026-10-20T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["London, United Kingdom"],"countries":["GB"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":111000,"max_usd":205000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":25},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon SageMaker","optional":false},{"name":"AWS","optional":false},{"name":"CI/CD","optional":false},{"name":"IAM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"TensorFlow","optional":false},{"name":"XGBoost","optional":false},{"name":"Feature Store","optional":true}],"status":"live","first_seen_at":"2026-08-23T00:14:12Z","employer_posted_date":"2026-10-07","last_verified_at":"2026-10-20T06:22:54Z","board_verified":true,"closed_at":null,"days_open":58,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":58},"description":"Join our team as a Principal Machine Learning Engineer, where you will provide technical leadership across the full lifecycle of machine learning systems. You will define ML architecture, establish engineering standards, drive MLOps maturity, and ensure that our models are scalable, secure, explainable, and governed to enterprise-grade standards. This highly hands-on, technical role combines architectural vision with deep practical expertise in ML engineering and AWS-native MLOps.\nMissions\nDefinir la arquitectura de ML de extremo a extremo para la plataforma de emparejamiento, incluyendo tuberías de datos, flujos de trabajo de entrenamiento de modelos, tiempos de inferencia y ecosistemas de telemetría.\nLiderar la adopción de patrones de MLOps de mejor clase, herramientas de plataforma y capacidades de AWS SageMaker en todo el ciclo de vida del modelo.\nColaborar con equipos de plataforma, seguridad e ingeniería de datos para implementar una arquitectura de características orientada al lago de datos escalable y una gobernanza de ML de grado empresarial.\nProfil recherché\n - Proven track record architecting and delivering production ML systems at scale in enterprise environments- Experience shaping ML security practices, including cross-account IAM, data minimisation, and PII-safe design\n- Expertise in low-latency inference architectures and real-time model serving\n- Expert-level Python and ML Model frameworks (e.g. PyTorch, TensorFlow, XGBoost)\n- Deep expertise with AWS SageMaker (training, processing, pipelines, endpoints, registry) and complementary AWS services\n- Ability to influence architecture, mentor senior engineers, and set long-term technical direction\n- Strong thought leadership in MLOps automation, CI/CD for ML, and model lifecycle management\n- Strong grounding in drift detection, telemetry pipelines, observability patterns, and model QA\n- Advanced experience designing explainability systems, reason codes, and governance artefacts\n- Career Stage: Manager\n- Masters or PhD or equivalent experience in STEM desirable but not essential\n- Bachelors in a STEM subject, e.g. mathematics, physics, engineering, computer science, or adjacent degrees\n- Knowledge of distributed training, GPU/accelerator optimisation, and scaling strategies\n- Experience designing or governing multi-account AWS ML 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