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About This Role
Description: About Us
Cerium Networks is a lifecycle-driven technology partner. We bring together advanced systems integration and professional services to design, deliver, and optimize the platforms our clients depend on, spanning cybersecurity, networking, data center, collaboration, and customer experience solutions—all amplified by AI at every layer. From intelligent automation to data-driven insights, we help organizations harness AI to transform operations, enhance experiences, and accelerate business value, while our commitment to continuous innovation and lifecycle engagement creates meaningful growth opportunities for our associates to lead in a rapidly evolving, AI-driven world.
We are seeking an AI Engineer III to join our Data & AI implementation services team to help build and deliver custom-built features and software solutions on Cerium’s AI Agent Platform hosted on Azure. This is a builder role where you will be a hands-on developer creating and implementing software solutions for Cerium’s customers.
Primary responsibilities focus on designing, developing, and implementing secure and reliable generative AI solutions and application features to build agents and automate workflows for our clients. The position involves full-stack development including front-end interfaces, back-end services, integration with data platforms, and AI models and services working within our primary tech stack consisting of Python, FastAPI, LangChain / LangGraph, Microsoft Azure and Foundry services, and Azure DevOps.
What You’ll Do
- Independently own technical delivery for client engagements from architecture and estimation through implementation, testing, deployment, documentation and handoff of AI solutions such as agents, automation workflows, and Gen AI models with best practices for our clients.
- You will lead technical conversations with customers to gather requirements on the solutions we will build and establish the capabilities of our solutions and services.
- You will deploy software to our Agent Platform on Azure with CI/CD pipelines as needed for automated builds and releases.
- You will integrate custom AI solutions with 3rd party applications, modern data architecture, and cloud services.
- You will provide technical consulting services to propose solutions, gather feedback for necessary adjustments during the software development lifecycle, and perform administrative handoffs to ensure smooth transitions post-implementation.
- You will offer technical leadership and mentoring of junior engineers, providing technical direction, architecture and code reviews, and implementation guidance across in AI/ML, DevOps, security, and cloud architecture best practices.
- You will support pre-sales and sales teams in designing and proposing solutions with estimated levels of effort as needed.
Requirements: What We’re Looking For
Required
- Bachelor’s degree in software engineering, applied artificial intelligence, or a related field or equivalent experience.
- 7+ years of experience in full-stack development experience. Python and Azure experience.
- Proven track record deploying AI agents or Generative AI solutions with Python, FastAPI, LangChain / LangGraph, and Azure.
- Experience consulting with clients and translating complex technical concepts into simple, understandable terms for non-technical audiences.
- Ability to manage multiple tasks, projects, and clients simultaneously.
- Customer service skills and experience.
- Strong organization and documentation abilities.
- Willing to travel 10-20% of the time depending on client needs.
Preferred
- Experience with Azure Container Apps, Cosmos DB, and Azure Container Registry is a plus.
- 3+ years in consulting/professional services.
- DevSecOps skills and experience with Snyk or other vulnerability scanning tools.
- Ensuring software meets ISO 27001 compliance standards.
- Certifications or equivalent experience across AI and DevOps solutions: Microsoft Azure AI Apps and Agents Developer Associate (AI-103); Microsoft Certified Azure AI Cloud Developer Associate (AI-200); Microsoft Certified Azure Dev Associate / DevOps Engineer Expert (AZ-204 / AZ-400); Microsoft Azure Cosmos DB Developer Specialty (DP-420)
Location: Seattle, WA; Beaverton, OR
Schedule: Hybrid, 1-Day onsite
Compensation
- Pay Range: $110,000 - $150,000
- Actual compensation will vary and may be above or below the range based on various factors including but not limited to location, experience, and performance.
Benefits
- Medical, dental and vision Insurance
- Basic term life and AD&D insurance (fully paid for by the company)
- Paid time off annually: 15 days of PTO and 9 paid holidays
- 401(k) Plan and Employer Matching Contribution of 4%
- Wellness program that includes up to $500 in cash awards
- Employee Assistance Program
Salary Context
This $110K-$150K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Cerium Networks, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($130K) sits 39% below the category median. Disclosed range: $110K to $150K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Cerium Networks AI Hiring
Cerium Networks has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Beaverton, OR, US. Compensation range: $150K - $150K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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