Senior Data Engineer - AI Infrastructure Integration, High Performance Compute

Position Summary

Key Responsibilities

• Design, develop, test, validate, and deploy AI/ML-enabled capabilities that improve infrastructure reliability, capacity forecasting, observability, operational automation, and enterprise decision-making
• Apply natural language processing, statistical modeling, supervised learning, unsupervised learning, embeddings, classification, anomaly detection, forecasting, and optimization techniques to complex enterprise data sets
• Build reusable models, data pipelines, APIs, feature workflows, prompt libraries, automation components, dashboards, and integration patterns across technology, risk, operations, and platform domains
• Support the full model lifecycle, including use case intake, data preparation, model training, model selection, validation readiness, deployment, monitoring, ongoing performance review, and remediation planning
• Provide analytical and technical challenge to AI/ML solutions by assessing model design, assumptions, limitations, performance, controls, explainability, and implementation risks
• Partner with infrastructure, data science, model risk, cyber/risk, architecture, operations, and product teams to define requirements, success metrics, delivery plans, governance artifacts, and operational handoff criteria
• Develop production-grade code, reusable documentation, model artifacts, validation evidence, test automation, and implementation procedures aligned to enterprise engineering and governance standards
• Advance MLOps, CI/CD, version control, model serving, workflow orchestration, monitoring, and hybrid cloud deployment practices for AI-enabled infrastructure services
• Communicate technical findings, model outcomes, operational impact, implementation risks, and tradeoffs clearly to engineering teams, senior stakeholders, governance partners, and cross-functional leaders

Required Qualifications

• 15+ years of experience delivering data science, software engineering, analytics, automation, platform engineering, risk analytics, cloud engineering, SRE, or infrastructure technology solutions
• 7+ years of hands-on experience applying AI/ML, NLP, statistical modeling, predictive analytics, optimization, or quantitative methods to enterprise business, risk, technology, or operational problems
• Strong Python programming skills and practical experience with data science, machine learning, or NLP libraries such as pandas, NumPy, scikit-learn, TensorFlow, PyTorch, spaCy, Hugging Face Transformers, Gensim, or equivalent tools
• Experience with the end-to-end model lifecycle, including model ideation, data preparation, training, selection, validation, deployment, ongoing monitoring, performance review, and governance documentation
• Experience developing NLP, text analytics, classification, embeddings, recommendation, key driver analysis, network analysis, anomaly detection, or predictive modeling solutions
• Experience creating model documentation, validation evidence, implementation procedures, monitoring plans, governance artifacts, or peer review materials in a large enterprise environment
• Working knowledge of APIs, data pipelines, relational databases, SQL, dashboards, visualization tools, automation frameworks, version control, CI/CD, observability, and production support practices
• Ability to analyze complex structured and unstructured data, identify patterns, convert insights into engineering action, and quantify business or operational impact through metrics and reporting
• Demonstrated experience working in Agile delivery environments using tools such as Jira, Kanban boards, Confluence, and related delivery or documentation platforms
• Excellent written and verbal communication skills, with the ability to explain model behavior, technical findings, operational risks, governance requirements, and implementation tradeoffs to technical and executive audiences
• Highly motivated, self-directed, and comfortable operating across multiple initiatives in a large, matrixed, geographically distributed technology organization

Desired Qualifications

• BA or BS in Computer Science, Data Science, Engineering, Mathematics, Statistics, Information Systems, Artificial Intelligence, Business Analytics, Business Administration, or a related quantitative or technical field; advanced Masters degree preferred
• Experience developing AI/ML solutions for infrastructure operations, capacity forecasting, incident prediction, anomaly detection, root-cause analysis, configuration intelligence, automated remediation, or operational excellence
• Experience with generative AI, large language models, prompt engineering, reusable prompt libraries, AI-assisted workflows, model validation guidance, or GenAI governance practices
• Experience leading or managing data science, NLP, model governance, or AI enablement initiatives across multiple stakeholders or teams
• Experience with enterprise AI infrastructure platforms, model-serving frameworks, GPU or accelerated compute environments, Red Hat OpenShift AI, NVIDIA AI platforms, or comparable AI/ML infrastructure technologies
• Experience integrating AI solutions with enterprise monitoring, observability, workflow orchestration, API, dashboarding, or automation platforms such as Tableau, Streamlit, Shiny, Jupyter, or equivalent tools
• Experience working in regulated environments with model risk management, validation, peer review, data governance, privacy, security, audit, and compliance requirements
• Ability to influence technical direction, establish reusable processes, develop best practices, and communicate effectively with geographically dispersed engineering, operations, architecture, risk, and business partners

Skills

• Analytical Thinking
• Application Development
• Data Management
• Risk Management
• Solution Design
• Agile Practices
• Architecture
• Collaboration
• Decision Making
• DevOps Practices
• Business Acumen
• Data Quality Management
• Financial Management
• Solution Delivery Process
• Test Engineering