As a technology leader that is rapidly on the move, Harman is filled with people who are focused on making life better. Innovation, inclusivity and teamwork are a part of our DNA. When you add that to the challenges we take on and solve together, you'll discover that at Harman you can grow, make a difference and be proud of the work you do every day.
Principal 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 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).
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 scale—not 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