Principal AI Engineer

Overview

Lead AI solution design implementation and operationalization across Harman’s BI AI and Data ecosystem. Define how AI is applied at scale ensuring robust secure explainable testable production-ready solutions. Lead development of prebuilt AI integrations and custom AI/ML solutions while establishing enterprise standards for MLOps model governance and lifecycle management.

What You'll Do37

• 1AI Strategy & Technical Leadership Define best practices for AI solution design deployment and lifecycle management
• 2Use Case Prioritization Identify high-value AI opportunities and guide technical execution
• 3Standards & Governance Establish standards for model development validation deployment and monitoring
• 4Define architectural patterns for batch vs real-time inference feature engineering pipelines model reuse standardize implementation of forecasting frameworks classification pipelines anomaly detection frameworks NLP document intelligence pipelines
• 5Ensure solutions are modular reusable and scalable
• 6Data Pipeline Alignment Ensure AI solutions leverage enterprise data pipelines such as Databricks
• 7Guide feature design and data structures required for high-performing models
• 8Work with Platform Engineers on infrastructure compute and scalability
• 9Define and enforce MLOps standards using MLflow including experiment tracking model versioning promotion workflows Dev QA Prod co-design CI/CD pipelines with Platform Engineering automated model testing validation gates before deployment
• 10Establish deployment patterns batch scoring pipelines scheduled retraining jobs model serving endpoints
• 11Define testing frameworks covering model performance validation data validation and schema enforcement backtesting especially for forecasting
• 12Establish standards for drift detection monitoring and alerting
• 13Drive adoption of explainability techniques SHAP feature importance business-level validation
• 14Define model governance standards model approval workflows version control rollback strategies auditability via MLflow and logging
• 15Ensure data access controls compliance traceability from raw data to models outputs
• 16Drive responsible AI practices bias detection mitigation transparency and explainability stakeholder engagement translate complex AI solutions into business value
• 17Technical Mentorship Guide AI Engineers support broader team development align AI initiatives with Data Engineering BI and Platform strategies stakeholder engagement translate complex AI solutions into business value
• 18Innovation & Continuous Improvement Evaluate emerging AI tools frameworks and capabilities drive improvements in AI tooling workflows scalability promote automation and reusable AI components
• 19Success looks like scalable production-ready reusable AI solutions governed traceable and continuously monitored standardized MLOps processes AI solutions integrated into data pipelines and business workflows
• 20Expert-level Python ML/AI frameworks strong hands-on MLflow experience building production-grade AI/ML systems on Databricks
• 21Standardizing AI patterns forecasting NLP anomaly detection classification
• 22Understanding data pipelines feature engineering dependencies model monitoring drift detection explainability SHAP
• 23AI security governance auditability requirements proven ability to define standards lead technical direction across teams
• 247+ years software engineering data engineering AI/ML engineering or related fields 3+ years designing deploying production AI/ML systems at enterprise scale
• 25Experience leading technical strategy architecture across multiple teams or business domains
• 26Experience designing deploying Generative AI using LLMs RAG vector search embeddings prompt engineering
• 27Bonus Generative AI with OpenAI Anthropic Gemini or similar foundation models
• 28Enterprise RAG architectures vector databases semantic search agent-based AI
• 29Experience with Databricks Mosaic AI Vector Search Model Serving Unity Catalog Lakehouse AI capabilities
• 30Cloud AI services Azure AWS or Google Cloud Platform deploying AI workloads using Kubernetes containerized architectures
• 31Feature stores online offline feature serving real-time inference systems
• 32Responsible AI frameworks model risk management regulatory compliance
• 33Experimentation platforms A/B testing causal inference
• 34Modern deep learning frameworks PyTorch TensorFlow Hugging Face
• 35Forecasting optimization recommendation systems supply chain analytics manufacturing AI
• 36AI platform strategy enterprise-wide AI transformation
• 37Advanced degree MS or PhD Computer Science Artificial Intelligence Machine Learning Statistics Applied Mathematics or related field

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