Description & Requirements About the RoleAs 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 Do1.

AI Strategy & Technical LeadershipAI 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 & DevelopmentDefine architectural patterns for:

Batch vs real-time inferenceFeature engineering pipelinesModel reuse across use casesStandardize implementation of common AI solutions:

Forecasting frameworksClassification pipelinesAnomaly detection frameworksNLP/document intelligence pipelinesEnsure solutions are modular, reusable, and scalable 3.

Data & Platform IntegrationData 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 ManagementDefine and enforce MLOps standards using MLflow, including:

Experiment trackingModel versioning and registryPromotion workflows (Dev QA Prod)Co-design CI/CD pipelines with

Platform Engineering:

Automated model testingValidation gates before deploymentEnvironment consistency across stagesEstablish deployment patterns:

Batch scoring pipelinesScheduled retraining jobsModel serving endpoints where needed 5. Testing, Validation & TrustDefine testing frameworks covering:

Model performance validationData validation and schema enforcementBacktesting (especially for forecasting)Establish standards for:

Drift detection (data + model)Monitoring and alertingDrive adoption of:

Explainability techniques (SHAP, feature importance)Business-level validation (not just statistical metrics) 6. Security, Governance & Responsible AIDefine model governance standards:

Model approval workflowsVersion control and rollback strategiesAuditability via MLflow and logging

Ensure:

Data access controls and complianceTraceability from raw data features models outputsDrive responsible AI practices:

Bias detection and mitigationTransparency and explainability where required 7.

Cross-Functional Leadership & MentorshipTechnical 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 ImprovementTechnology 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 LikeAI solutions are scalable, production-ready, and reusable across use casesModels are governed, traceable, and continuously monitoredMLOps processes (MLflow, CI/CD) are standardized and widely adoptedAI solutions are deeply integrated into data pipelines and business workflowsThe organization consistently delivers reliable, trusted AI at scale-not experiments What You Need to Be SuccessfulExpert-level Python and deep experience with ML/AI frameworksStrong hands-on experience with MLflow (tracking, registry, lifecycle management)Deep experience building and deploying production-grade AI/ML systems on DatabricksStrong experience with MLOps, CI/CD pipelines, and model lifecycle governanceExperience standardizing AI patterns (forecasting, NLP, anomaly detection, classification)Strong understanding of data pipelines and feature engineering dependenciesExperience with model monitoring, drift detection, and explainability techniques (e.g., SHAP)Strong understanding of AI security, governance, and auditability requirementsProven ability to define standards and lead technical direction across teams7+ years of experience in software engineering, data engineering, AI/ML engineering, or related technical fields3+ years designing and deploying production AI/ML systems at enterprise scaleExperience leading technical strategy and architecture across multiple teams or business domainsExperience designing and deploying Generative AI solutions using LLMsExperience with Retrieval-Augmented Generation (RAG), vector search, embeddings, and prompt engineering Bonus Points if You HaveExperience implementing Generative AI solutions using OpenAI, Anthropic, Gemini, or similar foundation modelsExperience building enterprise RAG architectures, vector databases, semantic search, and agent-based AI solutionsExperience with Databricks Mosaic AI, Vector Search, Model Serving,... For full info follow application link. HARMAN is an Equal Opportunity /Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or Protected Veterans status.