Principle AI Engineer

HARMAN International•Novi, MI

2d•$125,250 - $183,700

About The Position

As the Principal AI Engineer, you will act as the technical leader for AI solution design, implementation, and operationalization across Harman’s BI, AI, and Data ecosystem. Your primary focus will be defining how AI is applied at scale—ensuring solutions are robust, secure, explainable, testable, and production-ready. You will lead the development of both prebuilt AI integrations and custom AI/ML solutions, while establishing enterprise standards for MLOps, model governance, and lifecycle management. You will ensure AI solutions are not isolated experiments, but fully integrated, scalable systems built on top of the data platform (Databricks).

Requirements

Expert-level Python and deep experience with ML/AI frameworks
Strong hands-on experience with MLflow (tracking, registry, lifecycle management)
Deep experience building and deploying production-grade AI/ML systems on Databricks
Strong experience with MLOps, CI/CD pipelines, and model lifecycle governance
Experience standardizing AI patterns (forecasting, NLP, anomaly detection, classification)
Strong understanding of data pipelines and feature engineering dependencies
Experience with model monitoring, drift detection, and explainability techniques (e.g., SHAP)
Strong understanding of AI security, governance, and auditability requirements
Proven ability to define standards and lead technical direction across teams
7+ years of experience in software engineering, data engineering, AI/ML engineering, or related technical fields
3+ years designing and deploying production AI/ML systems at enterprise scale
Experience leading technical strategy and architecture across multiple teams or business domains
Experience designing and deploying Generative AI solutions using LLMs
Experience with Retrieval-Augmented Generation (RAG), vector search, embeddings, and prompt engineering

Nice To Haves

Experience implementing Generative AI solutions using OpenAI, Anthropic, Gemini, or similar foundation models
Experience building enterprise RAG architectures, vector databases, semantic search, and agent-based AI solutions
Experience with Databricks Mosaic AI, Vector Search, Model Serving, Unity Catalog, and Lakehouse AI capabilities
Experience with cloud AI services on Azure, AWS, or Google Cloud Platform
Experience deploying and operating AI workloads using Kubernetes and containerized architectures
Experience with feature stores, online/offline feature serving, and real-time inference systems
Experience implementing Responsible AI frameworks, model risk management, and regulatory compliance requirements
Experience with experimentation platforms, A/B testing, and causal inference methodologies
Familiarity with modern deep learning frameworks including PyTorch, TensorFlow, and Hugging Face ecosystems
Experience supporting forecasting, optimization, recommendation systems, supply chain analytics, or manufacturing AI use cases
Experience contributing to AI platform strategy and enterprise-wide AI transformation initiatives
Advanced degree (MS or PhD) in Computer Science, Artificial Intelligence, Machine Learning, Statistics, Applied Mathematics, or a related field

Responsibilities

Define best practices for AI solution design, deployment, and lifecycle management.
Identify high-value AI opportunities and guide their technical execution.
Establish standards for model development, validation, deployment, and monitoring.
Define architectural patterns for batch vs real-time inference, feature engineering pipelines, model reuse across use cases.
Standardize implementation of common AI solutions: Forecasting frameworks, Classification pipelines, Anomaly detection frameworks, NLP/document intelligence pipelines.
Ensure solutions are modular, reusable, and scalable.
Ensure AI solutions effectively leverage enterprise data pipelines (e.g., Databricks).
Guide design of features and data structures required for high-performing models.
Work closely with Platform Engineers on infrastructure, compute, and scalability.
Define and enforce MLOps standards using MLflow, including experiment tracking, model versioning and registry, and promotion workflows (Dev → QA → Prod).
Co-design CI/CD pipelines with Platform Engineering: Automated model testing, Validation gates before deployment, Environment consistency across stages.
Establish deployment patterns: Batch scoring pipelines, Scheduled retraining jobs, Model serving endpoints where needed.
Define testing frameworks covering model performance validation, data validation and schema enforcement, backtesting (especially for forecasting).
Establish standards for drift detection (data + model) and monitoring and alerting.
Drive adoption of explainability techniques (SHAP, feature importance) and business-level validation (not just statistical metrics).
Define model governance standards: Model approval workflows, Version control and rollback strategies, Auditability via MLflow and logging.
Ensure data access controls and compliance, and traceability from raw data → features → models → outputs.
Drive responsible AI practices: Bias detection and mitigation, Transparency and explainability where required.
Guide AI Engineers and support broader team development.
Align AI initiatives with Data Engineering, BI, and Platform strategies.
Translate complex AI solutions into business value and ensure adoption.
Continuously assess emerging AI tools, frameworks, and capabilities.
Drive improvements in AI tooling, workflows, and scalability.
Promote automation and reusable AI components.