Senior Data Engineer – AI Infrastructure Integration, High Performance Compute

Jobtailor; New Jersey; On-site; USD 180,000 - 240,000; Full time

Job summary

Jobtailor is looking for an experienced AI/ML Platform Engineer to design, develop, test, validate, and deploy AI/ML capabilities that support infrastructure reliability, capacity forecasting, observability, and automation.

You will apply NLP, statistical modeling, embeddings, anomaly detection, and optimization across data pipelines, APIs, dashboards, and production systems, while guiding the full model lifecycle with cross-team collaboration.

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 problems.
• Strong Python programming skills.
• Practical experience with pandas, NumPy, scikit-learn, TensorFlow, PyTorch, spaCy, Hugging Face Transformers, Gensim, or equivalent tools.
• Experience with the end-to-end model lifecycle.
• 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.
• 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 and quantify business or operational impact through metrics and reporting.
• Demonstrated experience in Agile delivery environments using Jira, Kanban boards, Confluence, and related platforms.
• Excellent written and verbal communication skills.
• Ability to operate across multiple initiatives in a large, matrixed, geographically distributed technology organization.
• BA or BS in a related quantitative or technical field is listed under Desired Qualifications; advanced Master’s degree preferred.

Responsibilities

• Design, develop, test, validate, and deploy AI/ML-enabled capabilities for infrastructure reliability, capacity forecasting, observability, operational automation, and enterprise decision-making.
• Apply NLP, statistical modeling, supervised and unsupervised learning, embeddings, classification, anomaly detection, forecasting, and optimization techniques.
• Build reusable models, data pipelines, APIs, feature workflows, prompt libraries, automation components, dashboards, and integration patterns.
• Support the full model lifecycle from use-case intake and data preparation through deployment, monitoring, performance review, and remediation planning.
• Assess model design, assumptions, limitations, performance, controls, explainability, and implementation risks.
• Partner with infrastructure, data science, model risk, cyber/risk, architecture, operations, and product teams.
• Define requirements, success metrics, delivery plans, governance artifacts, and operational handoff criteria.
• Develop production-grade code, documentation, model artifacts, validation evidence, test automation, and implementation procedures.
• Advance MLOps, CI/CD, version control, model serving, workflow orchestration, monitoring, and hybrid cloud deployment practices.
• Communicate technical findings, model outcomes, operational impact, risks, and tradeoffs to technical and executive audiences.

Skills

Python Programming

NLP Techniques

MLOps Practices

Agile Delivery

Data Modeling

Education

BA/BS in a quantitative field

Tools

TensorFlow

scikit-learn

Pandas

NumPy

SpaCy

Hugging Face Transformers

Gensim

Jira

Confluence

CI/CD

Job description

• Design, develop, test, validate, and deploy AI/ML-enabled capabilities for infrastructure reliability, capacity forecasting, observability, operational automation, and enterprise decision-making
• Apply NLP, statistical modeling, supervised and unsupervised learning, embeddings, classification, anomaly detection, forecasting, and optimization techniques
• Build reusable models, data pipelines, APIs, feature workflows, prompt libraries, automation components, dashboards, and integration patterns
• Support the full model lifecycle from use-case intake and data preparation through deployment, monitoring, performance review, and remediation planning
• Assess model design, assumptions, limitations, performance, controls, explainability, and implementation risks
• Partner with infrastructure, data science, model risk, cyber/risk, architecture, operations, and product teams
• Define requirements, success metrics, delivery plans, governance artifacts, and operational handoff criteria
• Develop production-grade code, documentation, model artifacts, validation evidence, test automation, and implementation procedures
• Advance MLOps, CI/CD, version control, model serving, workflow orchestration, monitoring, and hybrid cloud deployment practices
• Communicate technical findings, model outcomes, operational impact, risks, and tradeoffs to technical and executive audiences

Requirements

• 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 problems
• Strong Python programming skills
• Practical experience with pandas, NumPy, scikit-learn, TensorFlow, PyTorch, spaCy, Hugging Face Transformers, Gensim, or equivalent tools
• Experience with the end-to-end model lifecycle
• 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
• 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 and quantify business or operational impact through metrics and reporting
• Demonstrated experience in Agile delivery environments using Jira, Kanban boards, Confluence, and related platforms
• Excellent written and verbal communication skills
• Ability to operate across multiple initiatives in a large, matrixed, geographically distributed technology organization
• BA or BS in a related quantitative or technical field is listed under Desired Qualifications; advanced Master’s degree preferred

Core Competencies

Demonstrates expertise in AI/ML model development, including NLP, statistical modeling, and optimization techniques, while effectively managing the full model lifecycle and collaborating across diverse teams. Proficient in Python and familiar with tools such as TensorFlow and scikit-learn to deliver impactful data-driven solutions.

Highest-signal resume keywords

• AI/ML Model Development
• Python Programming
• NLP Techniques
• MLOps Practices
• Agile Delivery

Hard Skills

• Statistical Modeling
• Predictive Analytics
• Optimization Techniques
• Data Pipeline Development
• Model Lifecycle Management
• Anomaly Detection
• Classification Techniques
• Feature Engineering
• Quantitative Methods
• Production-Grade Code Development

Soft Skills

• Excellent Communication Skills
• Collaboration Across Teams
• Analytical Thinking
• Problem-Solving

Industry Keywords

• Infrastructure Reliability
• Operational Automation
• Capacity Forecasting
• Observability
• Model Risk
• Data Science
• Cloud Engineering
• SRE
• Analytics
• Automation

Tools & Technologies

• TensorFlow
• Scikit-learn
• Pandas
• NumPy
• SpaCy
• Hugging Face Transformers
• Gensim
• Jira
• Confluence
• CI/CD