{"id":920937,"url":"https://alion.io/job/the-vanguard-group-aiml-engineer","title":"AI/ML Engineer","company":{"id":253,"name":"The Vanguard Group","domain":"vanguard.com","url":"https://alion.io/company/vanguard","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":75,"open_postings":73,"ghost_share":0.123,"stale_share":0.575,"repost_share":0.205,"time_to_fill_p50_days":28,"computed_at":"2026-11-04T06:34:25Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Toronto, Canada"],"countries":["CA"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":89000,"max_usd":163000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":74},"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Amazon ECS","optional":false},{"name":"Amazon EventBridge","optional":false},{"name":"Amazon S3","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":"ETL/ELT","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Semantic Search","optional":false},{"name":"Amazon Kinesis","optional":true},{"name":"Apache Kafka","optional":true},{"name":"Flink","optional":true}],"status":"live","first_seen_at":"2026-09-14T00:00:00Z","employer_posted_date":"2026-09-14","last_verified_at":"2026-11-04T05:28:05Z","board_verified":true,"closed_at":null,"days_open":51,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":51},"description":"At Vanguard's Corporate Services division, we are seeking a Machine Learning/ AI Engineer to design, build, and scale enterprise AI/ML solutions that drive business innovation and support the development, deployment, and operationalization of intelligent applications.The ideal candidate combines strong software engineering, machine learning, and cloud expertise with hands-on experience delivering production-grade AI solutions. This role will partner closely with business stakeholders, product teams, and engineers to solve complex business problems using machine learning, Generative AI, and advanced analytics.\nResponsibilities\nDesign, develop, and deploy end-to-end AI/ML solutions, including data ingestion, feature engineering, model training, deployment, monitoring, and lifecycle management.\n\nBuild scalable, cloud-native AI/ML applications and services using AWS technologies such as SageMaker, ECS, Lambda, S3, EventBridge, and Step Functions.\n\nDevelop and maintain machine learning, data engineering, and MLOps pipelines supporting batch and real-time workloads.\n\nDesign and implement Generative AI solutions leveraging Large Language Models (LLMs), Advanced RAG, vector databases, knowledge retrieval systems, agentic AI frameworks, and fine-tuning techniques.\n\nDesign and utilize knowledge graphs, graph databases, and relationship-based analytics to enhance enterprise intelligence and decision-making.\n\nPartner with business stakeholders to translate business challenges into scalable analytical and AI-driven solutions.\n\nConduct data discovery and exploratory analysis, establish data lineage, and perform root cause analysis to ensure data quality and reliability.\n\nImplement model monitoring, observability, alerting, and operational support processes for production AI/ML solutions.\n\nEnsure adherence to enterprise AI governance, security, Responsible AI, privacy, and model risk management standards.\n\nServe as a machine learning engineering subject matter expert, lead technical design discussions, and mentor team members on AI/ML best practices.\n\nStay current on emerging AI technologies and evaluate their application to business opportunities.\n\nQualifications\nBachelor's degree in Computer Science, Engineering, Data Science, Mathematics, or a related technical field; Master's degree preferred.\n\n6+ years of experience in Machine Learning Engineering, Data Engineering, Software Engineering, or a related discipline.\n\n3+ years of hands-on experience building scalable data pipelines and ETL solutions using AWS services.\n\nStrong proficiency in Python and modern software engineering practices.\n\nExperience deploying and supporting production-grade AI/ML applications in cloud environments, preferably AWS.\n\nStrong experience with SageMaker, MLOps, CI/CD pipelines, model deployment, monitoring, and Machine Learning Development Lifecycle (MDLC) practices.\n\nExperience with containerization and orchestration technologies such as Docker, ECS, and Kubernetes.\n\nExperience with Generative AI technologies, including LLMs, Advanced RAG, vector databases, semantic search, agentic AI frameworks, and enterprise knowledge retrieval systems.\n\nExperience designing and implementing knowledge graph solutions and graph databases.\n\nStrong understanding of software engineering fundamentals, including system design, testing, security, observability, and version control.\n\nAbility to lead technical initiatives, influence architectural decisions, and collaborate effectively across business and technology teams.\n\nPreferred Experience\nReal-time data processing and streaming technologies such as Kafka, Flink, or Kinesis.\n\nAI governance, Responsible AI, and model risk management frameworks.\n\nEnterprise-scale AI platform development and solution architecture.\n\nHow We Work\nVanguard has implemented a hybrid working model for the majority of our crew members, designed to capture the benefits of enhanced flexibility while enabling in-person learning, collaboration, and connection. 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