AI Engineer – Reliability & Warranty Analytics

In this role you will help design develop and productionize AI-driven solutions that enhance existing analytics products and enable new capabilities across warranty forecasting reliability risk analysis engineering decision support and intelligent research workflows. You will partner closely with data scientists software engineers product leads and business stakeholders to translate complex problems into practical AI systems that improve efficiency insight generation and business impact.

This position is especially well suited for an engineer who enjoys applying modern AI approaches in a highly technical environment not just building demos but creating robust governed production-ready solutions that support high-value decision-making.

Youll be expected to:

1. Productization of Reliability & Warranty Analytics Solutions

• Embed AI capabilities into existing analytics products improving usability scalability and insight generation.
• Translate complex probabilistic outputs into actionable decision-ready insights for business partners (e.g. recall decisions cost forecasts supplier recovery strategies).
• Develop intelligent interfaces that enable users to explore model results uncertainty ranges and scenario outcomes through AI-assisted interaction.
• Improve system efficiency by modernizing analytical workflows and migrating prototype models into robust production-grade pipelines.

2. AI-Driven Decision Support & Risk Interpretation

• Build AI tools that enhance interpretability of probabilistic outputs including:
  • automated explanation of model assumptions and uncertainty
  • natural-language summaries of risk analysis results
  • anomaly detection and early warning signals
• Develop intelligent workflows that assist with root cause investigation population stratification and defect identification using combined statistical and AI approaches.
• Enable decision support for high-impact processes such as recall risk assessment regulatory response and warranty reserve forecasting.

3. Agentic AI & Knowledge Intelligence for Analytics Workflows

• Design and deploy agentic AI systems that support:
  • literature and technical research for reliability methodologies
  • internal knowledge discovery across analytics documentation and prior studies
  • automated generation of modeling insights summaries and technical reports
• Build retrieval-augmented systems that connect internal data external research and business context to accelerate model development and innovation.
• Develop reusable AI agents that integrate with enterprise tools to support analytics experimentation and decision-making workflows.

4. Data Integration & Scalable AI Pipelines

• Build scalable pipelines that integrate structured warranty data (e.g. claims exposure production) with unstructured sources such as documents reports and research literature.
• Implement efficient data handling strategies for large-scale reliability datasets including time-dependent covariates calendarized data and hierarchical aggregations.
• Ensure robust data validation lineage tracking and governance compliance across DEV/QA/PROD environments.

5. Advanced Model Evaluation Validation & Governance

• Develop frameworks for evaluating probabilistic and AI models including:
  • backtesting out-of-sample validation and calibration
  • benchmarking across statistical ML and hybrid approaches
  • uncertainty validation and confidence interval robustness
• Implement observability monitoring and guardrails for AI-enhanced systems to ensure reliability accuracy and responsible usage.
• Contribute to enterprise standards for model governance documentation and reproducibility in alignment with analytics protocol requirements.

6. Collaboration & Technical Leadership

• Work closely with data scientists engineers and product teams to translate complex reliability problems into scalable AI-driven solutions.
• Partner with stakeholders to define analytical requirements for new capabilities and advanced forecasting models.
• Communicate technical findings clearly to both technical and non-technical audiences enabling data-driven decision-making.

Contribute to best practices in AI engineering statistical modeling and software craftsmanship helping elevate the technical maturity of the organization

• Bachelors degree in Computer Science Artificial Intelligence Data Science Statistics Mathematics Engineering Information Systems or a related quantitative discipline or an equivalent combination of education and relevant experience.
• 3 years of professional experience in software engineering machine learning engineering AI engineering or data science with strong software delivery skills.
• 2 years of hands-on experience building and deploying machine learning or advanced analytics solutions in production or near-production environments.
• Strong proficiency in Python for AI application development data processing model integration and workflow automation.
• Experience with modern AI/ML frameworks and packages such as scikit-learn pandas NumPy PyTorch TensorFlow or similar tools.
• Experience building applications or services on GCP especially with technologies such as BigQuery Vertex AI Cloud Run GCS and related cloud-native tooling.
• Experience integrating diverse data sources through SQL APIs enterprise data platforms and vector/retrieval-based patterns.
• Demonstrated ability to build more than surface-level chat experiences including agentic workflows multi-step reasoning patterns tool use/function calling orchestration and evaluation pipelines.
• Experience with software engineering fundamentals including version control CI/CD testing logging monitoring and production support.
• Strong communication skills and the ability to work effectively with both technical and non-technical stakeholders.

Even better you may have

• Masters degree or PhD in Computer Science AI Statistics Applied Mathematics Engineering or a related field.
• Experience operationalizing AI for forecasting reliability analytics risk modeling optimization or enterprise decision support.
• Familiarity with statistical modeling approaches such as Bayesian methods time series forecasting survival/reliability modeling or probabilistic decision frameworks.
• Experience building RAG systems enterprise knowledge assistants or AI tools that interact with internal documentation and collaboration platforms.
• Experience with orchestration and agent frameworks such as LangGraph LangChain ADK or similar ecosystems.
• Experience with observability evaluation frameworks prompt/version management safety guardrails and governance controls for AI systems.
• Ability to take ambiguous business challenges and convert them into clear technical designs phased implementation plans and measurable business outcomes.
• Experience working in cross-functional product environments with data scientists analysts software engineers and business customers.
• A strong bias for building solutions that are scalable maintainable and usable not just technically interesting.