Principal Software Engineer

Solera•Westlake, TX

About The Position

We're looking for a pragmatic, hands-on Principal AI Engineer who gets things done. You'll spend significant time writing code while helping elevate the technical skills of the broader organization. This role is ideal for someone who thrives on building and delivering AI-powered products and features, has extensive experience leveraging AI development tools to accelerate delivery, and excels at establishing AI strategy and best practices across an entire fleet management portfolio. You'll balance individual contribution with strategic technical leadership, serving as the subject matter expert (SME) for AI technology adoption, helping teams integrate AI capabilities into applications, and mentoring engineers on how to develop effectively with AI-assisted tools.

Requirements

10+ years of professional software engineering experience
3+ years focused on AI/ML product development and delivery
2+ years in a technical leadership position
Proven track record of shipping AI-powered features to production at scale
Extensive experience using AI-assisted development tools in daily workflows
History of establishing AI practices and strategies across engineering organizations
Willingness to maintain hands-on technical involvement
Expert-level experience building production AI/ML applications
Strong background in supervised and unsupervised learning algorithms
Hands-on experience with deep learning frameworks (TensorFlow, PyTorch, JAX)
Production experience with Large Language Models (LLMs) and generative AI
Knowledge of prompt engineering, RAG (Retrieval Augmented Generation), and fine-tuning
Experience with computer vision for applications like driver monitoring or vehicle inspection
Understanding of time-series analysis and forecasting for fleet operations
Experience with recommender systems and optimization algorithms
Deep expertise using AI coding assistants (GitHub Copilot, Cursor, Cody, etc.) in production
Proven ability to train teams on effective AI-assisted development practices
Understanding of prompt engineering for code generation and debugging
Knowledge of when and how to leverage AI tools for maximum productivity
Experience establishing organizational standards for AI tool usage
Predictive maintenance and failure prediction models
Route optimization and dynamic routing algorithms
Driver behavior analysis and safety scoring
Fuel efficiency optimization and cost reduction
Demand forecasting and capacity planning
Natural language processing for logs, reports, and documentation
Anomaly detection for vehicle health and operations
Computer vision for safety, compliance, and inspections
Designing and implementing ML pipelines and model serving infrastructure
Experience with ML platforms (AWS SageMaker, Azure ML, Google Vertex AI, Databricks)
Knowledge of model versioning, experiment tracking (MLflow, Weights & Biases)
Building automated retraining and monitoring pipelines
Understanding of model deployment patterns (batch, real-time, edge)
Experience with model compression and optimization for production
Feature stores and data versioning tools
Expert-level proficiency in Java and ML/AI libraries (SpringAI, LangChain4J)
Strong experience with modern backend development (SpringBoot, SpringCloud, Java21+alk, or similar)
Hands-on experience with cloud platforms (AWS, Azure, or GCP)
Proficiency with containerization (Docker) and orchestration (Kubernetes)
Experience with vector databases (Pinecone, Weaviate, Chroma) for RAG applications
Strong background in both relational and NoSQL databases
Knowledge of data engineering and ETL pipelines
Understanding of AI ethics, bias detection, and fairness
Experience with model explainability techniques (SHAP, LIME)
Knowledge of AI compliance and regulatory requirements
Privacy-preserving ML techniques
Model monitoring for drift, degradation, and anomalies
Identifying high-ROI AI opportunities aligned with business goals
Building business cases for AI investments
Evaluating build vs. buy vs. partner decisions for AI capabilities
Understanding AI cost optimization and resource management
Exceptional communication skills with ability to influence engineering and product leadership
Strong mentorship mindset with proven impact on elevating teams
Ability to translate complex AI concepts for non-technical stakeholders
Track record of making architectural decisions and defending technical choices
Collaborative mindset focused on enabling AI adoption across teams
Experience creating training programs and educational content
Bias toward action and shipping working AI solutions
Passion for AI innovation balanced with pragmatic delivery
Strong problem-solving and debugging skills for complex AI systems
Ability to manage multiple AI initiatives simultaneously
Comfortable challenging the status quo constructively
Customer-centric mindset when designing AI features

Nice To Haves

PhD or Master's degree in Computer Science, AI/ML, or related field
Experience in fleet management, transportation, logistics, or IoT domains preferred
Experience with edge AI and model deployment on IoT devices
Knowledge of reinforcement learning for optimization problems
Familiarity with federated learning for distributed data
Experience with AI-powered analytics and business intelligence
Background in conversational AI and chatbot development
Understanding of multimodal AI (vision + language)
Experience with AutoML and neural architecture search
Knowledge of graph neural networks for route optimization
Familiarity with geospatial AI and mapping technologies
Experience with Electronic Logging Device (ELD) data analysis
Background in telematics and sensor data processing
Understanding of regulatory compliance in transportation (FMCSA, DOT)
Experience with AI security and adversarial ML

Responsibilities

Design and implement production AI features and capabilities across the fleet management portfolio
Build scalable AI/ML models and services for predictive maintenance, route optimization, driver behavior analysis, and fleet operations
Develop AI-powered APIs and microservices that serve multiple web and mobile applications
Leverage and evangelize AI-powered development tools (GitHub Copilot, Cursor, ChatGPT, Claude, etc.) to accelerate feature development
Create reusable AI components, SDKs, and libraries that reduce duplication across teams
Modernize legacy systems by integrating modern AI capabilities
Implement LLM-powered features (chatbots, natural language interfaces, document processing, automated insights)
Build AI experimentation frameworks and A/B testing infrastructure
Define the long-term AI strategy and roadmap for the fleet management platform
Serve as the organizational SME for AI technology adoption and implementation
Champion and integrate emerging AI technologies that solve real business problems
Mentor engineers on AI/ML best practices, prompt engineering, and AI-assisted development
Establish standards for responsible AI development, model governance, and ethical AI use
Share best practices for using AI development tools to maximize productivity
Guide architectural decisions for AI feature integration across the platform
Foster a culture of AI innovation, experimentation, and continuous learning
Partner with product, engineering, and business teams to identify high-impact AI opportunities
Evaluate and select appropriate AI/ML frameworks, models, and platforms for different use cases
Design MLOps pipelines for model training, deployment, monitoring, and retraining
Implement responsible AI practices including bias detection, fairness, and explainability
Create comprehensive documentation, playbooks, and training materials for AI adoption
Build monitoring and observability for AI model performance and drift detection
Collaborate with data teams on feature engineering, data pipelines, and model training infrastructure
Drive proof-of-concepts and experiments to validate AI opportunities