{"id":2353013,"url":"https://alion.io/job/constructor-tech-ml-ops-engineer","title":"ML Ops Engineer","company":{"id":1984326,"name":"Constructor Tech","domain":"constructor.tech","url":"https://alion.io/company/constructor-tech","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Singapore","Bulgaria"],"countries":["SG","BG"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":61000,"max_usd":131000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":25},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"GCP","optional":false},{"name":"GitLab CI","optional":false},{"name":"Kubeflow","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"PyTorch","optional":false},{"name":"Terraform","optional":false},{"name":"Feature Store","optional":true}],"status":"live","first_seen_at":"2026-06-24T11:01:31Z","employer_posted_date":"2026-08-18","last_verified_at":"2026-10-13T02:29:08Z","board_verified":true,"closed_at":null,"days_open":110,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":110},"description":"Our mission\nConstructor’s mission is to enable all educational organisations to provide high-quality digital education to 10x people with 10x efficiency. \nWith strong expertise in machine intelligence and data science, Constructor’s all-in-one platform for education and research addresses today’s pressing educational challenges: access inequality, tech clutter, and low engagement of students.\nPlease send your resume in English only.\nBrief Job Description:\nBuilds and maintains the infrastructure and tooling that keeps machine learning systems reliable in production - from designing CI/CD pipelines and model deployment workflows to monitoring performance and managing model lifecycle at scale. The role works closely with ML, backend, and platform teams, contributes to automation frameworks and observability standards, and helps ensure AI models move seamlessly from experimentation to production. Requires 5+ years of MLOps or DevOps engineering experience, with a track record of operating robust ML infrastructure in production-grade environments.\nMission:\nMake the path from model experiment to reliable production as fast, automated, and observable as possible.\nEducation:\nBachelor’s degree or higher in Computer Science, Engineering, or a related field - or equivalent practical experience.\nDuties and Responsibilities:\nDesign and maintain CI/CD pipelines for ML models and services.\nBuild and operate model deployment, serving, and rollback workflows.\nImplement monitoring, observability, and alerting for models in production.\nManage the model lifecycle with MLflow: experiment tracking, versioning, registries, and reproducibility.\nAutomate infrastructure and partner with ML and platform teams on standards.\nQualifications & Experience:\n5+ years of MLOps or DevOps engineering experience.\nStrong with containers and orchestration (Docker, Kubernetes).\nExperience with CI/CD (GitLab CI) and infrastructure-as-code (Terraform or similar).\nHands-on with a major cloud platform (Azure, AWS, or GCP).\nHands-on experience with MLflow for experiment tracking and model registry.\nFamiliarity with ML frameworks (PyTorch), workflow orchestration (Kubeflow or similar), and monitoring stacks.\nNice to Have (Not Obligatory):\nExperience serving LLMs or large models in production.\nKnowledge of feature stores and data pipeline tooling.\nCost and latency optimisation for model serving.\nWhat We Offer\n Choice of work equipment (e.g., laptop, monitor, etc.)\n English classes (iTalki - $130 monthly)\n Flexible schedule (we usually work between 09:00/10:00 and 18:00/19:00)\n Newborn bonus (€500 per child)\n Patent remuneration\n Paid leave\n Remote work in locations without our offices\n Hybrid work in locations with offices (2 days in-office, 3 days remote)\nConstructor fosters equal opportunity for people of all backgrounds and identities. We are led by a gender-balanced board committed to building a diverse and inclusive organisation where everyone can become their best self. We do not discriminate based on age, disability, gender identity, sexual orientation, ethnicity, race, religion or belief, parental and family status, or other protected characteristics. We welcome applications from women, men and non-binary candidates of all ethnicities and socio-economic backgrounds. 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