{"id":2590864,"url":"https://alion.io/job/mcdermott-senior-it-pillar-specialist","title":"Senior IT Pillar Specialist","company":{"id":1877428,"name":"McDermott","domain":"mcdermott.com","url":"https://alion.io/company/mcdermott-com","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":{"grade":"A","score":88,"open_postings":152,"ghost_share":0,"stale_share":0.487,"repost_share":0,"time_to_fill_p50_days":51,"computed_at":"2026-10-30T05:48:23Z"}},"role":"Support","role_family":"Support","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Kuala Lumpur, Malaysia"],"countries":["MY"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":12000,"max_usd":32000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":881},"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CI/CD","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false}],"status":"live","first_seen_at":"2026-06-12T16:20:51Z","employer_posted_date":"2026-06-12","last_verified_at":"2026-10-30T21:43:04Z","board_verified":true,"closed_at":null,"days_open":140,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":138},"description":"Job Overview:\nThe AI/ML Developer for Engineering IT is responsible for designing, integrating, deploying, and supporting AI/ML solutions that enhance engineering applications, enterprise platforms, and digital workflows. This role focuses on translating business and engineering requirements into scalable, secure, and production-ready AI capabilities, while enabling responsible AI adoption, operational reliability, and continuous improvement across Engineering IT systems.\n Key Tasks and Responsibilities:\nSupport the deployment, integration, and lifecycle management of AI/ML solutions within Engineering IT platforms, engineering applications, and enterprise workflows\nCollaborate with engineering application owners, business stakeholders, infrastructure teams, and vendors to identify, design, and implement AI-enabled use cases that improve engineering productivity, quality, and operational efficiency\nDevelop and maintain production-ready pipelines for model deployment, monitoring, retraining, and version control, following MLOps and DevOps best practices\nEnsure secure, scalable, and reliable integration of AI/ML services with enterprise systems, databases, APIs, and cloud or on-premises environments\nEstablish processes for model performance monitoring, drift detection, incident resolution, and continuous improvement to ensure stable business operations\nWork closely with data, cybersecurity, architecture, and compliance teams to ensure responsible AI adoption, data governance, privacy, and adherence to enterprise standards\nPrepare technical documentation, support materials, and knowledge transfer artifacts for Engineering IT teams, end users, and support personnel\nProvide application support and troubleshooting for AI/ML-enabled engineering solutions, including issue analysis, root cause identification, and coordination of fixes with relevant teams\nContribute to evaluation of emerging AI tools, frameworks, and engineering technology platforms to recommend practical solutions aligned with Engineering IT strategy\nParticipate in cross-functional projects, pilot programs, and digital transformation initiatives to drive adoption of AI capabilities across engineering and project delivery functions\n Essential Qualifications and Education:\n2-5 years of hands-on experience in AI/ML development, deployment, or integration, with exposure to production environments\nBachelor’s or Master’s degree in Computer Science, Engineering, Artificial Intelligence, Data Science, Mathematics, or a related discipline\nStrong programming capability in Python, with practical knowledge of machine learning libraries and frameworks\nSolid understanding of machine learning concepts, model development lifecycle, model evaluation, and performance tuning\nExperience integrating AI/ML solutions with enterprise or engineering applications using APIs, services, and system interfaces\nWorking knowledge of MLOps and DevOps practices, including version control, CI/CD, model deployment, monitoring, and retraining workflows\nFamiliarity with cloud and on-premises deployment environments, containerization, and scalable solution architecture\nUnderstanding of data pipelines, data 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