{"id":3474328,"url":"https://alion.io/job/alldus-senior-ai-ml-production-engineer-2","title":"Senior AI / ML Production Engineer","company":{"id":1044164,"name":"Alldus","domain":"alldus.com","url":"https://alion.io/company/alldus","size_band":null,"is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":"Schema","truth_index":{"grade":"C","score":68,"open_postings":6,"ghost_share":0.167,"stale_share":0.5,"repost_share":0.333,"time_to_fill_p50_days":67,"computed_at":"2026-11-04T06:34:25Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"contractor","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Dublin, Ireland"],"countries":["IE"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":90000,"max_usd":193000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":11},"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":"Databricks","optional":false},{"name":"Docker","optional":false},{"name":"DVC","optional":false},{"name":"FastAPI","optional":false},{"name":"Flask","optional":false},{"name":"Git","optional":false},{"name":"Kubeflow","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Python","optional":false},{"name":"Rest API","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-10-28T00:00:00Z","employer_posted_date":"2026-11-02","last_verified_at":"2026-11-03T12:24:07Z","board_verified":true,"closed_at":null,"days_open":7,"trust":{"level":"ok","repost_count":1,"flags":[],"days_open":7},"description":"Our client, a global life sciences organisation, is hiring a Senior AI/ML Production Engineer to joint heir team remotely on a 12-month daily rate contract. The successful candidate will take machine learning models from prototype to production, developing the services, pipelines and infrastructure needed to run reliable, scalable AI applications in clinical research.\nResponsibilities\nTurn Python prototypes and machine learning models into maintainable production services.\n\nDevelop and optimise Python applications, REST APIs, microservices and automated data pipelines.\n\nBuild CI/CD and MLOps pipelines for testing, deployment, model versioning, monitoring and reproducibility.\n\nImprove code performance, memory efficiency and reliability when processing large, complex datasets.\n\nDeploy AI/ML solutions on AWS and/or Azure using Docker, Kubernetes and cloud-native services.\n\nImplement logging, monitoring and model performance tracking, and troubleshoot production issues.\n\nWork with Data Science and Engineering teams to define deployment requirements and resolve technical challenges.\n\nApply secure engineering practices and support relevant data privacy and regulatory requirements.\n\nSkillset\nAt least 5 years of professional software engineering experience, including deploying and supporting AI/ML solutions in production.\n\nStrong Python skills, with experience writing maintainable, well-tested and efficient code.\n\nA track record of taking machine learning models or analytical applications from prototype to production.\n\nExperience building APIs and backend services using FastAPI, Flask or similar frameworks.\n\nPractical experience with MLOps, CI/CD and automated deployment, using tools such as MLflow, Kubeflow, Airflow, DVC or Databricks.\n\nExperience with AWS and/or Azure, alongside Docker and Kubernetes.\n\nAn understanding of model lifecycle management, monitoring and production troubleshooting.\n\nExperience with SQL databases, data pipelines and large-scale data processing.\n\nFamiliarity with Git, automated testing, code reviews and infrastructure 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