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About This Role
Sr. Director of Engineer \- AI Practice
Who We Are
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Solera is a global leader in data and software services that strives to transform every touchpoint of the vehicle lifecycle into a connected digital experience. In addition, we provide products and services to protect life’s other most important assets: our homes and digital identities. Today, Solera processes over 300 million digital transactions annually for approximately 235,000 partners and customers in more than 90 countries. Our 6,500 team members foster an uncommon, innovative culture and are dedicated to successfully bringing the future to bear today through cognitive answers, insights, algorithms and automation. For more information, please visit solera.com.
The Role
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We are looking for a passionate, entrepreneurial, hands\-on Engineering Sr. Director to manage an agile engineering team. In this role, you will be responsible for leading a team of application and front\-end / Back\-end engineers to drive innovation in full\-stack product development, while relentlessly improving performance, scalability, and maintainability.
We, at Solera, are looking to hire a Sr. Director of Engineering, who is a thought leader, and a world\-class engineer who has a proven record of creating an impact on business and engineering with little or no help. Professionals at this will have a deep impact across a wide variety of business and technology decisions spanning multiple projects.
We are looking for a strong technology leader with good understanding of AI Domain, Market Trends on Technology \& Sustainability to drive Innovation, Business Acumen to provide leadership and direction to a diverse group of professional engineers working with global Product Marketing \& Offering teams to build and support outstanding Services, Solution and Products.
What You’ll Do
Build reliable systems that integrate large language models into our products
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- Leverage AI tools like Copilot and automation platforms to enhance product capabilities and engineering efficiency.
- Develop and maintain web applications using .NET (C\#, ASP.NET Core/Framework), Java
- Develop and optimize database solutions using MS\-SQL Server, SQL Server, PostgreSQL, or NoSQL databases.
- Build Responsive and Dynamic user interfaces with React.JS
- Proficiency in using Unit testing frameworks to write robust test suites.
- Continuous Integration and Continuous Delivery Tools like Jenkins
- Collaborate with cross functional teams and other stakeholders.
- Implement RESTful APIs and integrate third\-party services.
- Write clean, maintainable, and efficient code following best practices
- Participate in code reviews, testing, and documentation.
- Stay up to date with emerging technologies and best practices in full\-stack development.
- What You’ll Bring
- Bachelor’s degree in computer science, Engineering, or a related field (or equivalent experience).
- 12\+ years of experience in software development
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- LLM Skills: Hands\-on experience building AI agents with Large Language Models (LLMs), including Retrieval\-Augmented Generation (RAG), as well as tuning models.
- LLM Model: Apt with GPTs, Llama, or any other LLM using frameworks such as LangChain, LangGraph. Knowledge of vector databases, memory systems, and human\-in\-the\-loop workflows.
- AI Tools:Hands\-on with AI\-driven development tools, intelligent code assistance, and workflow automation .
- Experience in working with product\-based companies, contributing to the development, enhancement, and scaling of high\-quality products (product lifecycle management).
- Strong proficiency in C\#, ASP.NET Core/.NET 8\+, Web API, Entity Framework Core.
- Expertise in React.js, TypeScript, Redux, Next.js (a plus).
- Experience with SQL Server, MS\-SQL Server or NoSQL databases (MongoDB, Redis).
- Knowledge of RESTful API development and integration.
- Search and analytics engine like Elastic Search.
- Experience with unit testing (xUnit, Jest, Mog) and integration testing.
- DevOps \& CI/CD \- Familiarity with Docker, Kubernetes, Azure DevOps.
- Ensure high performance, scalability, and security of applications.
- Conduct code reviews, write unit tests, and follow TDD and Agile development practices.
- Mentor junior developers and collaborate with UX/UI designers and product teams.
- Ensure application security, performance, and scalability.
- Excellent problem\-solving and analytical skills.
- Strong communication and collaboration abilities.
- Ability to work independently and take ownership of projects.
- LLM Skills: Experience with using LLMs using embeddings, RAG, VectorDB and prompt engineering building production grade applications.
Knowledge of the following technologies is a plus:
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- Experience with GraphQL, WebSocket, or SignalIR.
- Knowledge of Microservices architecture.
- Familiarity with Blazor or Angular.
- Experience with Infrastructure as Code (Terraform, Bicep).
- Development of Complex Application and System Architectures
- Queues like RabbitMQ, SQS.
- Hands on with Cloud (AWS / Azure) OR On Prem Data centers
*It is impossible to list every requirement for, or responsibility of, any position. Similarly, we cannot identify all the skills a position may require since job responsibilities and the Company’s needs may change over time. Therefore, the above job description is not comprehensive or* *exhaustive.* *The Company reserves the right to adjust, add to or eliminate any aspect of the above description. The Company also retains the right to require all employees to undertake additional or different job responsibilities when necessary to meet business needs.*
EQUAL OPPORTUNITY EMPLOYER
SOLERA HOLDINGS, INC., AND ITS US SUBSIDIARIES (TOGETHER, SOLERA) IS AN EQUAL EMPLOYMENT OPPORTUNITY EMPLOYER. THE FIRM'S POLICY IS NOT TO DISCRIMINATE AGAINST ANY APPLICANT OR EMPLOYEE BASED ON RACE, COLOR, RELIGION, NATIONAL ORIGIN, GENDER, AGE, SEXUAL ORIENTATION, GENDER IDENTITY OR EXPRESSION, MARITAL STATUS, MENTAL OR PHYSICAL DISABILITY, AND GENETIC INFORMATION, OR ANY OTHER BASIS PROTECTED BY APPLICABLE LAW. THE FIRM ALSO PROHIBITS HARASSMENT OF APPLICANTS OR EMPLOYEES BASED ON ANY OF THESE PROTECTED CATEGORIES.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Solera, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Solera AI Hiring
Solera has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Westlake, TX, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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