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About This Role
### Company overview:
Blue Orange Digital is a boutique data \& AI consultancy that delivers enterprise\-grade results. We design and build modern data platforms, analytics, and ML/AI Agent solutions for mid‑market and enterprise clients across Private Equity, Financial Services, Healthcare, and Retail.
Our teams work with technologies like Databricks, Snowflake, dbt, and the broader Microsoft ecosystem to turn messy, real\-world data into trustworthy, actionable insight.
We’re a builder‑led, client‑first culture that prizes ownership, clear communication, and shipping high‑impact work.
Note: Please submit your resume in English, as all application materials must be in English for review and consideration.
### About the Role
We are looking for an AI Solutions Architect to design and deliver AI solutions for clients, with an immediate focus on a major international engagement. This is a hands\-on, client\-facing role for someone who architects AI systems, contributes to complex technical engagements, and works shoulder to shoulder with client teams to ship production\-grade work.
You will own technical workstreams from discovery through delivery, pairing the depth of a strong engineer with the communication skills of a trusted advisor. You speak the language of both the technology and the business, and you bring the credibility that comes from building and shipping real AI systems, not just advising on them. This role reports into our engineering leadership and offers a clear path to grow your scope and seniority as you build a track record of client impact.
### Your AI\-Augmented Workflow
At Blue Orange you operate with an augmented workflow powered by AI tools and virtual agents. From client research and architecture documentation to competitive analysis and proposal support, your automation stack handles the heavy lifting so you can focus on solving hard technical problems and building client trust. You continuously refine and expand this toolkit as part of your daily practice, modeling the tech\-forward culture we bring to our clients.
### What You Will Do
##### Technical Solution Delivery
- Architect and build end\-to\-end AI solutions for clients, from requirements through system design, technology selection, and hands\-on delivery. You make sure solutions are production\-grade, scalable, and aligned to business outcomes.
- Contribute hands\-on across AI/ML pipelines, agentic systems, LLM deployments, cloud infrastructure (AWS, Azure, Databricks, Snowflake), and modern data platforms.
- Participate in technical deep\-dives, design reviews, and architecture sessions with client engineering teams, holding a high bar for code quality, testing, and documentation.
- Evaluate emerging AI frameworks, foundation models, and tooling, separating what is production\-ready from what is hype and bringing the best forward for your clients.
##### Client\-Facing Work
- Serve as a trusted technical contact for client stakeholders, supporting AI strategy workshops, technology assessments, and roadmap sessions that translate AI capabilities into concrete business value.
- Build and maintain strong, trust\-based relationships with client technical leaders and engineers, embedding with their teams to accelerate delivery.
- Communicate clearly across audiences, explaining why an architecture decision matters to both a data engineer and a business sponsor.
##### Domain and Practice Development
- Develop domain expertise in the client's industry, understanding the business models, data ecosystems, and operational realities that shape how AI creates value.
- Build reusable reference architectures, solution accelerators, and patterns that enable faster delivery and stronger proposals.
- Support pre\-sales activities by scoping engagements, validating technical feasibility, and identifying expansion opportunities within accounts.
### What You Bring
- 5 to 8 years of progressive experience in data engineering, cloud architecture, AI/ML, or technology consulting, with a track record of hands\-on technical delivery.
- Solid technical credibility in AI/ML, at least one major cloud platform (AWS, Azure, or GCP), and modern data infrastructure (Databricks, Snowflake, dbt), plus working familiarity with agentic AI, LLMs, and generative AI.
- Experience contributing to data and AI transformation projects for enterprise clients, with the ability to own a workstream end to end.
- Strong client\-facing skills, comfortable pairing with client engineers and presenting your work to technical and business stakeholders.
- A clear communicator who translates complex technical concepts into business impact.
- Self\-directed and resourceful, comfortable figuring things out and taking ownership of your work.
- Comfort operating within an AI\-augmented workflow, actively using automation tools to amplify your output.
### Nice to Have
- Hands\-on experience building and deploying production AI/ML systems at scale, not just advisory work.
- Gaming experience: Experience in the gaming industry (including casino and integrated resort gaming), which is a strong advantage for this engagement.
- Hospitality experience: Experience in hospitality, entertainment, or resort and leisure businesses.
- Degree in Computer Science, Data Science, AI/ML, or a related technical field.
- Prior experience at a boutique or mid\-size consulting firm where you wore multiple hats and operated with autonomy.
- Multilingual capability and international experience.
### Travel and Location
This role is US remote, supporting an international client engagement, so you should be open to periodic travel abroad, including on\-site visits with our client in Australia. These trips are occasional and typically run a couple of weeks at a time, giving you focused time on the ground to partner closely with client teams. All travel, accommodation, and per diem costs are fully covered.
### Why Blue Orange
- AI\-first, not AI\-curious: Build and ship production AI systems every day, working at the cutting edge of the technology rather than writing slide decks about it.
- Hands\-on culture: Blue Orange values depth. Our best people go from architecture to code review to client conversation in the same day.
- Tech\-forward workflow: Your augmented workflow and automation stack are not a perk, they are how we work, letting you move faster and go deeper than anyone at a traditional firm.
- Real growth path: Start strong as an individual contributor with a clear path to grow your scope, seniority, and leadership responsibility as you build client trust and a track record of impact.
- Elite team: With a high talent bar, you will work alongside some of the best AI engineers, data scientists, and strategists in the industry.
##### Compensation and Benefits
- Base Salary: $180,000
- Bonus Structure: Utilization\-based performance bonus tied to billable hours. Minimum utilization 50%, bonus eligibility begins at 70%, scaling upward with a target of 80% or more.
- Healthcare: Medical, dental, vision, and life insurance.
- Retirement: 401(k) matching.
- Time Off: Unlimited PTO, paid parental and bereavement leave.
- Development: Conference speaking, thought leadership platform, and increasing scope over time.
- Events: All conference, travel, accommodation, and per diem costs are covered.
Background checks may be required for certain positions and projects.
Blue Orange Digital is an equal\-opportunity employer.
Salary Context
This $170K-$185K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Blue Orange Digital, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. Disclosed range: $170K to $185K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Blue Orange Digital AI Hiring
Blue Orange Digital has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $185K - $185K.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 tracked positions. That's 5% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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.
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