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.

You’ll 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

• Bachelor’s 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…

• Master’s 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.