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About This Role
Overview:
At eimagine, we know that your best work happens when you live your best life and share your unique talents, so we do everything we can to be intentional in a remote enabled environment to make that possible. Recognized as a Best Places to Work since 2015, we are a team of humbly confident people who are proud of their craft, continuous learners, and have been known to cheer loudly for our teammates. For over 25 years we have been helping clients navigate technology and business change, while staying committed to delivering value & outcomes that enables their success.
Due to continued growth, we are seeking an AI Solutions Architect to lead the design and delivery of scalable, enterprise-grade AI solutions that solve complex business challenges for our clients. This role operates at the intersection of technology and strategy—bringing together software engineering, data science, and machine learning to architect solutions that are not only technically sound but aligned to business outcomes. As a senior leader, you will guide clients through ambiguity, shape AI strategies, and ensure solutions are built for scale, sustainability, and measurable impact. You will play a critical role in translating emerging AI capabilities into practical, high-value applications. If you’re passionate about turning emerging technologies into real-world solutions and want to partner with clients to create meaningful business value, this could be your next step. Join us as we #eimaginebetter.
Description of Duties:
- Lead the design of end-to-end AI/ML solutions, ensuring scalability, reliability, and alignment with client business objectives
- Translate ambiguous business challenges into clear, actionable AI strategies and solution architectures
- Define and guide implementation of enterprise AI systems, including data pipelines, model development workflows, and production inference architectures
- Architect scalable model-serving and distributed processing solutions that perform reliably in real-world environments
- Partner with client stakeholders to shape roadmaps, prioritize initiatives, and align technology decisions with business value
- Provide technical leadership across delivery teams, ensuring best practices in design, development, and deployment
- Evaluate and select appropriate tools, platforms, and frameworks to meet client needs across cloud ecosystems such as Microsoft Azure, Amazon Web Services, and Google Cloud
- Establish standards for performance, scalability, security, and governance across AI solutions
- Analyze system performance, model behavior, and data pipelines to identify optimization opportunities and improve outcomes
- Guide the responsible and ethical use of AI, including explainability, risk mitigation, and governance practices
- Mentor engineers and contribute to the growth and maturity of eimagine’s AI capabilities and delivery frameworks
- Stay at the forefront of emerging AI technologies and translate innovation into practical client recommendations
Desired Skills & Experience:
- 5+ years of experience in AI, machine learning, or advanced analytics, with a strong track record in architecting and delivering enterprise solutions
- Proven experience in a consulting or client-facing role, with the ability to influence stakeholders and drive strategic conversations
- Deep experience designing and deploying AI/ML solutions in cloud environments such as Microsoft Azure, Amazon Web Services, or Google Cloud
- Strong understanding of scalable system design, distributed processing, and production-grade AI architectures
- Experience with modern ML frameworks such as PyTorch, TensorFlow, or similar tools
- Experience integrating and operationalizing large language models (LLMs), including APIs such as OpenAI, Azure OpenAI, or Gemini
- Familiarity with generative AI patterns such as retrieval-augmented generation (RAG) and prompt engineering in enterprise contexts
- Strong foundation in data architecture, data pipelines, and data governance practices
- Experience with DevOps and MLOps practices, including CI/CD, containerization (Docker), and orchestration (Kubernetes preferred)
- Proficiency in Python and the ability to guide engineering teams on implementation approaches
- Exceptional problem-solving and critical thinking skills, with the ability to navigate ambiguity and make informed trade-offs
- Relevant cloud or AI certifications (Azure, AWS, GCP) are a plus
Education:
- Bachelor’s degree in information technology, computer science or equivalent job-related experience required.
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At eimagine, 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 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
eimagine AI Hiring
eimagine has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Indianapolis, IN, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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