Interested in this AI/ML Engineer role at Blue Orange Digital?
Apply Now →Skills & Technologies
About This Role
### Company Overview
Blue Orange Digital is a cloud\-based data transformation and predictive analytics development firm with offices in NYC and Washington, DC. From startups to Fortune 500s, we help companies make sense of their business challenges by applying modern data analytics techniques, visualizations, and AI/ML. Founded by engineers, we love passionate technologists and data analysts. Our startup DNA means everyone on the team makes a direct contribution to the growth of the company.
With 250\+ production deployments, a 95% strategy\-to\-build conversion rate, and delivery speeds 3–4x faster than traditional consulting, Blue Orange operates at the cutting edge of generative AI, agentic systems, and machine learning. We are scaling our practice by vertical — and we need senior technical leaders who can anchor an industry domain with hands\-on credibility and client trust.
### Position Overview
We are looking for a Principal AI Solutions Architect to serve as our most senior technical IC within a designated industry vertical — such as financial services, healthcare, private equity, retail, or media. This is a hands\-on, client\-facing role for someone who architects AI solutions, leads complex technical engagements, and becomes the person clients call when the problem is hard.
The ideal candidate has a career arc similar to our current senior technical leaders: a progression from hands\-on data engineering and cloud architecture through AI/ML solution design, evolving into a strategic, client\-facing technologist who can go deep with engineering teams and wide with executive stakeholders. You speak the language of both the technology and the industry you serve — and you bring the credibility that comes from having built and shipped production AI systems, not just advised on them.
### Your Growth Path
This role is explicitly designed as an IC\-to\-practice\-lead trajectory. On day one, your job is to be the best solutions architect in your vertical — winning client trust, delivering exceptional technical work, and building domain expertise that differentiates Blue Orange in the market. Over time, as you accumulate wins and develop relationships, you will have the opportunity to grow into full practice leadership: owning a P\&L, building and managing a team, and setting the strategic direction for your vertical. We will invest in your development with executive coaching, leadership exposure, and increasing scope — but the foundation is technical excellence and client impact first.
### Your AI\-Augmented Workflow
As a Principal at Blue Orange, you will operate with an augmented workflow powered by your own automation suite of AI tools and virtual agents. From client research and architecture documentation to competitive analysis and proposal development, your digital labor force handles the heavy lifting so you can focus on what matters most: solving hard technical problems, building client trust, and designing systems that deliver real business impact. You will continuously refine and expand this bot ecosystem as part of your daily practice — modeling the tech\-forward culture we bring to our clients.
### What You’ll Do
##### Technical Solution Leadership
- Architect end\-to\-end AI solutions for clients in your vertical — from discovery and requirements through system design, technology selection, and delivery oversight. You own the technical vision for your engagements and ensure solutions are production\-grade, scalable, and aligned to business outcomes.
- Provide hands\-on architectural guidance across AI/ML pipelines, agentic systems, LLM deployments, cloud infrastructure (AWS, Azure, Databricks, Snowflake), and modern data platforms — drawing on real experience building and shipping these systems in production.
- Lead technical deep\-dives, design reviews, and architecture sessions with client engineering teams. You set the technical bar for quality, and delivery teams look to you for guidance on complex problems.
- Evaluate emerging AI frameworks, foundation models, agentic architectures, and tooling — separating what’s production\-ready from what’s hype, and bringing the best forward for your clients and your vertical.
##### Client\-Facing Expertise
- Serve as the trusted technical advisor to senior stakeholders within your vertical — leading AI strategy workshops, technology assessments, and roadmap sessions that translate cutting\-edge AI capabilities into concrete business value.
- Conduct AI readiness assessments, technology due diligence, and build business cases that connect AI investments to measurable ROI — speaking the language of both the technology and the industry.
- Build and maintain deep, trust\-based relationships with client technical leaders and executives. You are the person they call when they need honest, expert guidance on their AI strategy.
- Present at industry conferences, author thought leadership content, and represent Blue Orange in technical forums — building the firm’s reputation as the definitive AI partner for your vertical.
##### Vertical Domain Development
- Develop deep domain expertise in your vertical — understanding the business models, regulatory landscape, data ecosystems, and competitive dynamics that shape how AI creates value in your industry.
- Build a library of reference architectures, solution accelerators, and reusable patterns tailored to your vertical — enabling faster delivery, stronger proposals, and differentiated positioning.
- Identify the highest\-value AI use cases for your industry and codify them into repeatable engagement frameworks that can scale across multiple clients.
- Leverage your orchestrated bot network and automation stack for client research, competitive intelligence, industry benchmarking, and proposal development — operating at a speed and depth that traditional architects cannot match.
##### Pre\-Sales \& Growth Support
- Partner with Account Executives, PE Channel Partners, and the sales team to support pre\-sales activities within your vertical — scoping engagements, validating technical feasibility, and presenting to prospective clients with both technical authority and industry context.
- Identify expansion opportunities within existing client accounts, leveraging your domain expertise and client relationships to spot where AI can solve adjacent problems.
- Provide market feedback to leadership on client needs, competitive dynamics, and emerging opportunities in your vertical — shaping how Blue Orange invests and positions itself.
##### Who You Are
- 10–15\+ years of progressive experience in data engineering, cloud architecture, AI/ML, and technology consulting — with a clear trajectory from hands\-on technical roles to strategic, client\-facing solution architecture.
- Deep, current technical credibility in AI/ML, cloud platforms (AWS, Azure, GCP), modern data infrastructure (Databricks, Snowflake, dbt), and emerging areas like agentic AI, LLMs, and generative AI. You can whiteboard an architecture, review code, and present to a board — all in the same week.
- Genuine expertise in a specific industry vertical (e.g., financial services, healthcare, private equity, retail, media, energy). You understand the business models, regulatory landscape, data ecosystems, and decision\-making patterns of your domain — not just the technology.
- Proven track record of leading complex AI and data transformation engagements for enterprise clients, PE portfolio companies, or Fortune 500 organizations.
- Exceptional client\-facing skills — comfortable leading technical workshops with engineering teams and strategy sessions with C\-suite stakeholders. You build trust through depth, not just polish.
- Strong communicator who translates complex technical concepts into business impact language. You can explain why an agentic architecture matters to a CFO as clearly as you can explain it to a data engineer.
- Self\-directed and entrepreneurial — you thrive in environments where you have to figure things out, not wait to be told. You take ownership of your domain and treat it like your own business.
- Ambition to grow into practice leadership over time — you are excited by the prospect of eventually owning a P\&L, building a team, and setting strategic direction, but you know the path starts with technical excellence and client trust.
- Comfort operating within an AI\-augmented workflow — you actively leverage your bot fleet, virtual agents, and automation tools to amplify your output and model the tech\-forward culture Blue Orange is known for.
- Multilingual capability and international experience is a strong plus.
- Based in New York City or willing to relocate; hybrid work model with regular in\-office presence.
### Nice to Have
- Advanced degree (MS or PhD) in Computer Science, Data Science, AI/ML, or a related technical field.
- Hands\-on experience building and deploying production AI/ML systems at scale — not just advisory or architecture\-only roles.
- Published thought leadership, conference speaking, or recognized expertise in AI and your industry vertical.
- Experience founding, co\-founding, or scaling a data/AI practice, consulting vertical, or technical business unit.
- Familiarity with the private equity ecosystem — portfolio company dynamics, value creation planning, due diligence processes.
- Prior experience at a boutique or mid\-size consulting firm where you wore multiple hats and operated with startup\-level autonomy.
### Why Blue Orange
- IC\+ With a Real Growth Path: This isn’t a dead\-end senior IC role. It’s designed to evolve into vertical practice leadership — with P\&L, team, and strategic ownership — as you build the track record and client relationships to earn it.
- AI\-First, Not AI\-Curious: We build and ship production AI systems every day. You will architect solutions at the absolute cutting edge of technology, not write slide decks about it.
- Hands\-On Culture: Blue Orange values depth. We don’t hire architects who only draw boxes on whiteboards. Our best people can go from strategy to code review to client dinner in the same day.
- Tech\-Forward Workflow: Your augmented workflow and bot fleet aren’t a perk — they’re how we work. You will operate with AI tools that let you move faster and go deeper than anyone at a traditional firm.
- Elite Team: With a \<1% talent acceptance rate, you will work alongside some of the best AI engineers, data scientists, and strategists in the industry.
- Founder\-Led, Not Corporate: Report to the CTO and CEO. Your voice matters. Your ideas ship. Your impact is visible.
##### Compensation \& Benefits
- Base Salary: $180,000
- Bonus Structure: Utilization‑based performance bonus tied to billable hours
+ Minimum utilization: 50%
+ Bonus eligibility begins at 70% utilization
+ Bonus scales upward as utilization increases
+ Target utilization: 80%\+
- Healthcare: Medical, dental, vision, and life insurance
- Retirement: 401(k) matching
- Time Off: Unlimited PTO, paid parental and bereavement leave
- Work Setup: New York office\-based (hybrid) with flexibility for remote work and client travel
- Development: Executive coaching, conference speaking, thought leadership platform, increasing leadership scope
- Events: All conference, travel, accommodation, and per diem costs covered
Background checks may be required for certain positions/projects.
Blue Orange Digital is an equal\-opportunity employer.
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 4,133 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 $185,000 based on 13,200 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Blue Orange Digital AI Hiring
Blue Orange Digital has 4 open AI roles right now. They're hiring across AI/ML Engineer. Positions span New York, NY, US, Jersey City, NJ, US, Washington, DC, US. Compensation range: $185K - $185K.
Location Context
AI roles in New York pay a median of $211,000 across 2,760 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, 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 Safety roles lead at $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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.