{"id":2079134,"url":"https://alion.io/job/brillio-senior-aiml-engineer-r01572485","title":"Senior AI/ML Engineer - R01572485","company":{"id":3063,"name":"Brillio","domain":"brillio.com","url":"https://alion.io/company/brillio","size_band":"501-1000","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"B","score":76,"open_postings":43,"ghost_share":0,"stale_share":0.953,"repost_share":0,"time_to_fill_p50_days":29,"computed_at":"2026-10-28T05:59:10Z"}},"role":"AI/ML","role_family":"AI/ML","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":["Bengaluru, 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governance frameworks, and technical guardrails across AI solutions\nDrive platform-level technical decision-making and define reusable architecture patterns, standards, and reference models for AI and GenAI solutions\nCollaborate with business, data, technology, and platform teams to translate AI architecture principles into production-ready capabilities\nEvaluate emerging AI technologies and assess their applicability to enterprise AI platforms, supporting long-term scalability and business outcomes\nMaintain and improve AI model governance, version control, and documentation for ongoing operational excellence\nRequired Skills:\nExpertise in enterprise-scale AI platform design and operationalization\nDeep proficiency in model lifecycle management, MLOps/LLMOps, and AI observability\nAdvanced programming skills in Python and PySpark\nExperience with cloud-based AI platforms and modern data/AI architectures\nKnowledge of Responsible AI, AI governance, model risk, security, and compliance\nHands-on experience with KubeFlow and BentoML for ML pipeline orchestration\nCompetence in classification algorithms such as decision trees and SVM\nExperience with Great Expectations and Evidently AI for model validation\nStrong proficiency in regression analysis (linear and logistic)\nStatistical analysis and computing for large datasets\nPreferred Skills:\nExperience with GenAI architectures, large language models, and AI agents\nProficiency in advanced ML frameworks such as TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, or MXNet\nKnowledge of vector databases and AI orchestration\nUnderstanding of AI observability and Responsible AI frameworks\nExperience developing reusable AI architecture patterns and accelerators\nDesired Qualifications:\nBachelor's degree in Computer Science, Data Science, Statistics, Mathematics, or a closely related discipline\nCertification in Machine Learning or Data Science (e.g., TensorFlow Developer Certificate, Microsoft Certified: Azure 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