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About This Role
At Baker Electric we are energized by truly living our values - Team Builders, Relentless Follow Through, All-In, and Care for Others. When our team members work together with these things in mind, we do great things both individually and as an organization. Being an employee-owned company shows our drive for sustainable, long-term growth while enhancing the lives of our team members.
SUMMARY: The Director, Data, Analytics & AI provides strategic and operational leadership for Baker Electric’s enterprise data, analytics, integration, and artificial intelligence capabilities.
The Director establishes the vision, architecture, governance, talent, platforms, and delivery practices required to make trusted information available across the company and enable responsible, value-focused AI adoption. The role oversees the enterprise data platform, data engineering, integrations, data governance framework, analytics enablement, AI governance, enterprise AI capabilities, and the relevant vendor ecosystem.
This leader initially manages two Data Engineers and one AI Analyst, with anticipated growth as Baker’s data, integration, analytics, and AI capabilities mature.
ESSENTIAL DUTIES AND RESPONSIBILITIES:
Data, Analytics, and AI Strategy
- Develop and maintain a multiyear strategy and roadmap for enterprise business applications.
- Ensure application roadmaps align with company goals, operational needs, vendor direction, integration requirements, cybersecurity standards, and data strategy.
- Partner with business application owners to optimize commercial platforms before pursuing customization or new systems.
- Lead application lifecycle planning, rationalization, renewal, replacement, and retirement.
- Monitor vendor product roadmaps, including embedded AI capabilities, and assess their relevance to Baker.
Data Platform and Architecture
- Lead the architecture, evolution, reliability, scalability, and appropriate use of the enterprise data platform.
- Establish data architecture, modeling, ingestion, transformation, storage, lineage, quality, and access standards.
- Oversee Baker’s Microsoft Fabric environment and associated services and partners.
- Ensure architecture decisions support analytics, integrations, AI readiness, security, maintainability, and cost effectiveness.
- Manage the professional services relationship supporting data architecture and delivery.
Integration Strategy and Delivery
- Own enterprise integration strategy, patterns, architecture, engineering, monitoring, and lifecycle management.
- Lead integration delivery through internal data engineers and external partners.
- Establish standards for APIs, scheduled data movement, error handling, documentation, monitoring, security, and support.
- Partner with the Applications team and business system owners to translate functional requirements into supportable integrations.
- Maintain transparency into integration dependencies, technical debt, risks, and capacity.
Data Governance and Master Data
- Establish and operate Baker’s enterprise data governance framework.
- Define decision rights, standards, stewardship practices, definitions, ownership expectations, issue escalation, and data quality measures.
- Coordinate master data governance across applications and business functions.
- Reinforce that business functions own the meaning and quality of their data, while the Data, Analytics & AI team owns the governance framework, enabling platforms, architecture, and stewardship coordination.
- Partner with application owners and business data stewards to resolve cross-functional data issues.
Analytics and Business Intelligence
- Establish the enterprise approach to analytics, reporting, semantic models, metrics, dashboards, self-service enablement, and data literacy.
- Ensure enterprise metrics have agreed definitions and accountable business owners.
- Support business analysts and distributed analytics contributors with governed data products, standards, training, and reusable capabilities.
- Avoid centralizing all report development within IT.
- Prioritize analytics efforts based on business value, data readiness, reuse, and scalability.
Artificial Intelligence Strategy and Governance
- Lead Baker’s enterprise AI strategy, roadmap, governance model, use-case intake, evaluation standards, and value measurement.
- Oversee enterprise AI capabilities such as Microsoft Copilot, AI agents, AI-enabled analytics, and approved cross-functional AI platforms.
- Partner with Applications on AI features embedded in enterprise applications.
- Partner with IT Operations, Cybersecurity & Compliance, Legal, HR, and business owners to assess privacy, security, regulatory, intellectual property, data, and people impacts.
- Establish responsible AI practices without creating unnecessary barriers to innovation.
- Define pilots with measurable hypotheses, expected outcomes, owners, risk controls, and exit criteria.
- Monitor adoption, business value, usage, risk, and ongoing appropriateness.
Innovation and Emerging Technology
- Maintain awareness of relevant data, AI, automation, and emerging technology trends.
- Develop a disciplined process for identifying, assessing, piloting, scaling, or discontinuing emerging capabilities.
- Collaborate with business technology leaders to identify opportunities within estimating, preconstruction, VDC/BIM, project execution, field operations, finance, human capital, and operational support.
- Distinguish strategic innovation from novelty-driven technology acquisition.
Vendor and Financial Management
- Manage data, analytics, integration, and AI vendors and professional services partners.
- Participate in vendor selection, contracts, renewals, performance management, and escalations.
- Develop and manage budgets, forecasts, licensing plans, partner capacity, and investment recommendations.
- Assess whether capabilities should be delivered internally, through commercial platforms, or with strategic partners.
Cross-Functional Leadership
- Work as a peer with the Director, Business Applications & PMO and Director, IT Operations.
- Ensure data, integration, analytics, and AI initiatives incorporate application ownership, infrastructure, cybersecurity, compliance, support, business process, and adoption requirements.
- Partner with Operational Excellence, Construction Applications, Finance, Human Capital, Construction Applications, Preconstruction, VDC/BIM, field and project execution, and other business technology leaders.
- Help shape enterprise recommendations without positioning the Data, Analytics & AI function above other technology domains.
Team Leadership
- Lead, coach, and develop data engineering and AI team members.
- Establish clear roles, technical standards, delivery practices, career paths, documentation expectations, and succession plans.
- Scale the team thoughtfully through internal development, hiring, business-embedded capabilities, and professional services partners.
- Build a culture that combines technical excellence, business curiosity, responsible innovation, and reliable execution.
EXPERIENCE, EDUCATION:
- Bachelor’s degree in information systems, data science, computer science, engineering, business, or a related discipline, or equivalent relevant experience.
- Ten or more years of progressive experience across data, analytics, integration, enterprise architecture, business intelligence, or AI.
- Five or more years of leadership experience.
- Demonstrated experience building or operating enterprise data platforms and integration capabilities.
- Experience with analytics governance, data quality, master data, and distributed reporting models.
- Practical experience with enterprise AI strategy, governance, adoption, or implementation.
- Strong vendor, budget, and professional services management experience.
- Microsoft Fabric, Azure, Power BI, and Microsoft AI ecosystem experience strongly preferred.
- Construction or project-based industry experience preferred.
At Baker, we are proud to be an employee-owned company. When you join, you will have the opportunity for a great career and the financial benefits of being an owner. Through our ESOP (Employee Stock Ownership Plan), eligible employees can become part owners of the company. Instead of just receiving a paycheck, you will be given shares of the company's stock over time. As the company prospers, the value of these shares can increase, providing you with a potential wealth-building opportunity in addition to a competitive compensation package. The ESOP will enable you to share the company's success and growth. For more information regarding employee ownership at Baker Electric and its significant potential, click here:
Employee Stock Ownership Program | Baker Electric (baker-electric.com)
PAY TRANSPARENCY:
The starting salary for this opportunity range is listed. Other rewards may include annual bonus eligibility based on company and individual performance, short- and long-term incentives, and program-specific awards. Baker provides a variety of benefits to employees, including health insurance coverage, an employee wellness program, life and disability insurance, a retirement savings plan, an Employee-Owned Program (ESOP), paid holidays, and paid time off (PTO). A candidate’s salary history will not be used in compensation decisions. Please note that the compensation information is a good-faith estimate for this position. It assumes a rate based on location and experience.
ACKNOWLEDGEMENTS:
Baker Electric is an Equal Opportunity-Affirmative Action Employer / Disability / Veteran.
We thank all applicants in advance for their interest in this position; however, only those selected for an interview will be contacted. If you are selected for an interview, a Baker Recruiter will contact you directly from our organization with a @baker-electric.com email.
Salary Context
This $175K-$200K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 roles with salary data).
View full AI/ML Engineer salary data →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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At BAKER ELECTRIC, 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 $212,000 based on 11,866 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,250. This role's midpoint ($187K) sits 12% below the category median. Disclosed range: $175K to $200K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
BAKER ELECTRIC AI Hiring
BAKER ELECTRIC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Escondido, CA, US. Compensation range: $200K - $200K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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