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About This Role
Job title: Sr. AI Engineer
Department / Division: Artificial Intelligence / Technology
Salary Range: $115,419/yr \- $142,576/yr plus bonus
Location: Ada, MI (Onsite)
What we’re looking for
We’re looking for an experienced AI Engineer to help design, build, and operate production\-grade AI capabilities that are reused across the enterprise. This role sits at the intersection of hands\-on engineering, platform thinking, and cross\-team enablement.
You’ll work on agentic systems, retrieval\-augmented generation (RAG) pipelines, and reusable AI services that power real business use cases. Depending on experience, this role may focus on owning individual AI capabilities end\-to\-end or setting technical direction across multiple solutions and teams.
What you’ll do day to day:
- Design, build, and operate production AI systems including agentic workflows, RAG pipelines, and LLM\-powered services
- Partner with product managers and business stakeholders to translate ambiguous problems into well\-scoped, feasible AI solutions
- Implement multi\-agent architectures with memory, state management, tool orchestration, and guardrails
- Build and tune RAG systems (chunking strategies, embeddings, vector stores, hybrid search, reranking, citation grounding)
- Develop evaluation frameworks to measure quality, safety, latency, and cost using automated and human\-in\-the\-loop approaches
- Implement observability, monitoring, and incident response practices for AI systems in production
- Contribute to shared AI platform standards, reference architectures, and reusable components
- Support and enable other engineering teams through design reviews, pairing, documentation, and pattern libraries
For more experienced candidates, this role may also include defining reference architectures, influencing platform standards, mentoring multiple engineers, and guiding build\-vs\-buy decisions across teams.
Required Qualifications
- At least 6 years experience in Software Development or AI Engineering
- Hands\-on experience building and operating production LLM\-powered applications
- Experience with retrieval\-augmented generation (RAG), including embeddings, vector stores, and grounding strategies
- Experience designing agentic or multi\-step AI systems with tool use, orchestration, and state management
- Proficiency in Python; working knowledge of JavaScript/TypeScript and SQL
- Experience working in at least one major cloud AI platform (AWS, GCP, or Azure)
- Strong software engineering fundamentals: testing, code review, CI/CD, version control, and documentation
- Experience implementing monitoring, logging, and evaluation for AI systems in production
Ability to communicate technical trade\-offs clearly to both technical and non\-technical partners
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Skills to Be Successful in the Role
- Structured prompt engineering and prompt lifecycle management
- Evaluation and LLMOps practices (quality measurement, drift detection, rollback strategies)
- Systems thinking and comfort operating AI services at scale
- Strong collaboration and influence skills across engineering and business teams
- Clear technical writing (design docs, architecture diagrams, post\-incident reviews)
Curiosity and sound judgment when assessing new tools, frameworks, and vendors
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What’s Special About This Team
This team builds shared AI capabilities that other teams across the organization rely on. Instead of one\-off solutions, the focus is on creating secure, scalable, and reusable AI foundations that accelerate adoption across domains.
Engineers on this team work closely with product, platform, security, and functional technology partners. The environment balances hands\-on building with long\-term platform thinking, and the team plays a key role in shaping how AI is responsibly deployed at enterprise scale.
Salary Context
This $115K-$142K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Amway, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($128K) sits 41% below the category median. Disclosed range: $115K to $142K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Amway AI Hiring
Amway has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Ada, MI, US. Compensation range: $142K - $142K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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