Interested in this AI/ML Engineer role at Shannon & Wilson Inc.?
Apply Now →Skills & Technologies
About This Role
Description:
Shannon \& Wilson is a leading geotechnical engineering firm dedicated to delivering innovative solutions in infrastructure, transportation, and environmental projects. As part of our digital transformation journey, we are investing in modern data platforms and analytics capabilities to support better decision\-making and operational excellence.
Job Summary:
We are seeking a Data Engineer to manage, optimize, and evolve our new Microsoft\-based data platform (Microsoft Fabric, Azure SQL, Power BI, etc.). This role will be instrumental in ensuring that our engineers, supporting staff, and leadership have reliable, trusted access to data for project delivery, reporting, and strategic insights.
Responsibilities:
Platform Operations \& Maintenance
- Administer and monitor Microsoft Fabric (or Azure Synapse/Lakehouse), Azure SQL Databases, and Power BI workspaces.
- Ensure data pipelines, notebooks, and scheduled refreshes run reliably and on time.
- Implement monitoring, alerting, and cost optimization strategies for the platform.
Data Engineering \& Integration
- Build, maintain, and optimize ingestion pipelines from internal systems (ERP, project management software) and external data sources.
- Design and maintain clean, curated data models to support reporting and analytics needs.
- Collaborate with business analysts to ensure data structures meet business requirements.
Governance \& Security
- Apply data governance standards for naming conventions, schema design, and access control.
- Work with IT to enforce security policies, manage permissions, and ensure compliance with client and regulatory requirements.
Continuous Improvement
- Stay current with Microsoft’s data platform capabilities (Fabric, Power BI, Azure Data Factory, etc.).
- Recommend new features, automation, or process improvements to enhance reliability and usability.
- Partner with stakeholders to identify and implement new use cases for analytics and reporting.
Requirements:
- Bachelor’s degree in Computer Science, Data Engineering, or related field (or equivalent experience).
- 7 to 14 years of experience with Microsoft data stack (Azure SQL Database, Azure Data Factory, Synapse, Power BI, or Fabric).
- Strong SQL development skills and experience with data modeling (star/snowflake schema) required.
- Hands\-on experience with ETL/ELT pipelines and automation scheduling required.
- Familiarity with cloud\-based data security and access control practices.
- Experience with Microsoft Fabric Lakehouse and Mirrored Databases preferred.
- Familiarity with engineering or construction industry data (projects, cost, schedule) preferred.
- Python or Spark experience for data transformation preferred.
- Knowledge of DevOps/Git\-based version control for data pipelines preferred.
- Strong collaboration skills – able to work with project managers, analysts, and IT staff.
- Excellent troubleshooting and problem\-solving skills.
- Continuous improvement mindset with willingness to learn and adopt new technologies.
- Clear communication skills (both technical and business\-oriented).
Level Placement:
Typically, depending on an applicant’s education, experience, other qualifications they possess, if they are a fit for the company and local office culture, and the current business needs, a Mid\-Level Data Engineer typically would be placed at a Shannon \& Wilson Senior Professional I, II, or III level with annual compensation ranging from $110,000 \- $145,000\.
Shannon \& Wilson is committed to providing a comprehensive total rewards package that includes the following benefits:
- Employee stock ownership
- Medical, prescription, vision, dental, long\-term care and life insurance plans are offered to employees (and their families).
- Pre\-tax health and daycare FSA
- Profit sharing and 401(k) plans with annual safe\-harbor contributions
- 10 \- 20 paid vacation days per year for full\-time employees, based on years of experience (prorated for part\-time employment)
- Sick leave accrues based on state requirements
- 9 paid holidays per year \+ 1 personal holiday
- Paid volunteer day
- Paid time off for bereavement and jury duty
- Two weeks of paid parental leave
- Lunch \& Live wellness webinars and an Employee Assistance Program
- Mentorship Program
- Tuition Reimbursement
- Free parking
Shannon \& Wilson is an Equal Opportunity Employer
Shannon \& Wilson participates in the E\-Verify program.
Please note that non\-solicited resumes from external recruitment agencies will not be considered as introductions to our business, unless a preapproved agreement is in place and the external recruitment agency has been engaged to work on this specific vacancy.
Salary Context
This $110K-$145K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Shannon & Wilson Inc., 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($127K) sits 41% below the category median. Disclosed range: $110K to $145K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Shannon & Wilson Inc. AI Hiring
Shannon & Wilson Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Seattle, WA, US. Compensation range: $145K - $145K.
Location Context
AI roles in Seattle pay a median of $228,700 across 516 tracked positions. That's 6% above the national 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.