{"id":1349892,"url":"https://alion.io/job/global-ml-ops-engineer","title":"ML Ops Engineer","company":{"id":1771858,"name":"Global","domain":"global.com","url":"https://alion.io/company/global-com","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":null,"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":94000,"max_usd":257000,"period":"year","method":"role_country_seniority_unknown","sample_n":101},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon ECS","optional":false},{"name":"Amazon EKS","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"AWS","optional":false},{"name":"AWS Lambda","optional":false},{"name":"AWS Step Functions","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"Feature Store","optional":false},{"name":"Python","optional":false},{"name":"Snowflake","optional":false},{"name":"Terraform","optional":false},{"name":"Kubernetes","optional":true}],"status":"live","first_seen_at":"2026-09-24T00:00:00Z","employer_posted_date":"2026-09-24","last_verified_at":"2026-10-01T15:19:52Z","board_verified":true,"closed_at":null,"days_open":8,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":8},"description":"Accepting applications until:\n16 October 2026Job Description\nYour New Role\nMLOps Engineer\nGlobal:IQ is the team building our new intelligence platform, turning first-party and partner data into smarter, data-led media plans across Global’s audio and Outdoor inventory.\nAs a MLOps Engineerat Global, you’ll build the operational infrastructure that brings AI and ML models into production. You’ll own the platforms, pipelines and processes that let our Data Science teams deploy, monitor, retrain and govern models reliably at scale-from the ground up.\nKey Responsibilities\nML Infrastructure & Deployment (40%): Build automated pipelines for model training, validation and deployment, plus model registries, feature stores and inference services, with self-serve tooling for Data Science teams. Model Monitoring & Operations (30%): Implement monitoring, alerting and automated recovery for ML workloads-covering latency, data quality and drift-and own rollback, rollout and incident response. MLOps Governance & Best Practice (20%): Establish controls for model lineage, reproducibility and audit trails, and introduce ML-specific CI/CD, testing and release automation. Collaboration & Enablement (10%): Partner with Data Science, Data Engineering and Product, and mentor junior engineers to raise operational standards.\n\nWhat you will love about this role:\nThink Big: This is a true AI-driven product-ML isn’t a feature, it’s the product, and your infrastructure directly enables business value.\n\nOwn It: You’re not maintaining legacy systems-you’re establishing the MLOps patterns and standards that will scale for years.\n\nKeep it Simple: You’ll build pragmatic, reusable patterns that keep ML systems reliable and maintainable without over-engineering.\n\nBetter Together: Global:IQ is a tight collaboration between technical and commercial teams.\n\nWhat Success Looks Like\nIn your first few months, you’ll have:\nDefined a clear operating model between MLOps and the teams developing models.\n\nDelivered an end-to-end MLOps path for at least one production use case, from model handoff through deployment, monitoring and rollback.\n\nEstablished baseline standards for model versioning, environment management and deployment.\n\nImplemented monitoring and alerting across operational health, data quality and model performance.\n\nWhat You’ll Need\nMLOps experience: You’ve operationalised ML models in production, owning deployment, monitoring and lifecycle management.\n\nStrong programming: Production-quality, testable Python.\n\nCloud expertise: Deep AWS knowledge (SageMaker, Lambda, ECS/EKS, Step Functions); Snowflake a plus.\n\nMLOps tooling: Experiment tracking and registries, workflow orchestration, model serving and feature stores.\n\nCI/CD & IaC: ML-specific CI/CD, Terraform, Docker and test automation.\n\nCross-disciplinary communication: You translate between Data Science and Engineering and explain trade-offs to any audience.","description_format":"text","description_chars":2928,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Food & Beverages","Media & Entertainment","Broadcasting"],"lifecycle":[{"event":"open","at":"2026-09-27T17:51:46Z"}],"liveness":{"score":73,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.771,"p_room":0.945,"age_days":7,"expected_fill_days":13,"reasons":["conf:18","velocity","win:mid"],"computed_at":"2026-10-01T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/global-ml-ops-engineer","json_url":"https://alion.io/job/global-ml-ops-engineer.json","meta":{"generated_at":"2026-10-02T00:22:49Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":244,"day_limit":5000,"remaining_today":4756,"minute_limit":60,"resets_at":"2026-10-03T00:00:00Z"}}}