Principle AI Engineer

About the Role

As the Principal AI Engineer you will act as the technical leader for AI solution design implementation and operationalization across Harmans BI AI and Data ecosystem. Your primary focus will be defining how AI is applied at scaleensuring 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).

What You Will Do

1. AI Strategy & Technical Leadership

• AI Engineering Leadership:
• Define best practices for AI solution design deployment and lifecycle management.
• Use Case Prioritization:
• Identify high-value AI opportunities and guide their technical execution.
• Standards & Governance:
• Establish standards for model development validation deployment and monitoring.

2. AI Solution Architecture & Development

• 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

3. Data & Platform Integration

• Data Pipeline Alignment:
• Ensure AI solutions effectively leverage enterprise data pipelines (e.g. Databricks).
• Feature & Data Strategy:
• Guide design of features and data structures required for high-performing models.
• Platform Collaboration:
• Work closely with Platform Engineers on infrastructure compute and scalability.

4. MLOps CI/CD & Lifecycle Management

• Define and enforce MLOps standards using MLflow including:
• Experiment tracking
• Model versioning and registry
• 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

5. Testing Validation & Trust

• Define testing frameworks covering:
• Model performance validation
• Data validation and schema enforcement
• Backtesting (especially for forecasting)
• Establish standards for:
• Drift detection (data model)
• Monitoring and alerting
• Drive adoption of:
• Explainability techniques (SHAP feature importance)
• Business-level validation (not just statistical metrics)

6. Security Governance & Responsible AI

• Define model governance standards:
• Model approval workflows
• Version control and rollback strategies
• Auditability via MLflow and logging
• Ensure:
• Data access controls and compliance
• Traceability from raw data features models outputs
• Drive responsible AI practices:
• Bias detection and mitigation
• Transparency and explainability where required

7. Cross-Functional Leadership & Mentorship

• Technical Mentorship:
• Guide AI Engineers and support broader team development.
• Collaboration:
• Align AI initiatives with Data Engineering BI and Platform strategies.
• Stakeholder Engagement:
• Translate complex AI solutions into business value and ensure adoption.

8. Innovation & Continuous Improvement

• Technology Evaluation:
• Continuously assess emerging AI tools frameworks and capabilities.
• AI Platform Evolution:
• Drive improvements in AI tooling workflows and scalability.
• Automation & Efficiency:
• Promote automation and reusable AI components.

What Success Looks Like

• AI solutions are scalable production-ready and reusable across use cases
• Models are governed traceable and continuously monitored
• MLOps processes (MLflow CI/CD) are standardized and widely adopted
• AI solutions are deeply integrated into data pipelines and business workflows
• The organization consistently delivers reliable trusted AI at scalenot experiments

What You Need to Be Successful

• 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

Bonus Points if You Have

• 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

HARMAN is proud to be an Equal Opportunity / Affirmative Action employer. All qualified applicants will receive consideration for employment without regard torace religion color national origin gender (including pregnancy childbirth or related medical conditions) sexual orientation gender identity gender expression age status as a protected veteran status as an individual with a disability or other applicable legally protected characteristics.