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About This Role
### Company overview:
Blue Orange Digital is a data engineering and AI consultancy that builds production\-grade data platforms, ML systems, and analytics solutions for companies that take their data seriously. Our engineers ship code alongside client teams, and our strategists stay close to the build — so the roadmaps, operating models, and readiness assessments we hand clients are grounded in the realities of what BOD goes on to deliver.
Our clients include Fortune 500 enterprises, high\-growth startups, and a growing roster of private equity portfolio companies. We partner with Snowflake, Databricks, AWS, GCP, and Azure to deliver battle\-tested solutions that drive measurable ROI.
Note: Please submit your resume in English, as all application materials must be in English for review and consideration.
### Position overview:
Blue Orange Digital partners with private equity firms to bring AI to their portfolio companies, from initial assessment through production rollout. We are scaling that practice and need a Senior AI Transformation Consultant to lead client engagements end to end.
You will own the engagement lifecycle: discovery and readiness assessments, AI maturity scoring across business and product dimensions, roadmap design, ROI modeling, and executive communication. You will lead delivery pods built from BOD architects, engineers, and SMEs, working alongside our Principal AI Solutions Architect and Lead Databricks Architect. Your audience ranges from PE operating partners and CFOs to portfolio company CEOs, CTOs, and founders.
### Responsibilities:
- Lead client\-facing discovery and assessment engagements, including AI readiness and AI vulnerability scoring across business, data, and product dimensions
- Translate assessment findings into executive\-grade roadmaps with phased ROI projections
- Own the engagement plan and run weekly steering committees with client and PE stakeholders
- Partner with the Lead Databricks Architect and Senior AI Engineers on technical scoping and delivery
- Build trust with client engineering leaders and co\-design adoption paths so new AI capabilities land well with the people who will operate them — we are on the same team, working toward shared success
- Author leave\-behind playbooks, operating models, and reference materials that clients use long after the engagement ends
- Help BOD productize repeatable assessment, accelerator, and rollout offerings across the PE channel
### Requirements:
- 7\+ years in data and AI consulting, with 3\+ years in a client\-facing senior or manager role
- Track record running enterprise AI programs that connect C\-level alignment to delivery outcomes
- Big Four, MBB, or boutique consulting firm background, or equivalent advisory experience inside a data/AI\-forward enterprise
- Fluency with modern AI system concepts — large language models, retrieval\-augmented approaches, and agent\-based systems — and with modern data platforms like Databricks and Snowflake; strong enough to advise on use\-case prioritization, build\-vs\-buy decisions, and the evolving vendor landscape
- Demonstrated change management experience partnering with client engineering organizations, including working alongside skeptical or cautious technical teams
- ROI modeling, business case development, and executive presentation skills
- Comfortable contributing to engagement success across delivery quality, client satisfaction, and organic account expansion
### Preferred qualifications:
- PE or private capital portfolio advisory background
- Experience with operating model design, M\&A integration, or post\-merger technology rationalization
- Network in PE operating partner and portfolio CTO communities
- Familiarity with ISO 42001, NIST AI RMF, and the EU AI Act
##### Benefits:
- Fully remote
- Flexible Schedule
- Unlimited Paid Time Off (PTO)
- Paid parental/bereavement leave
- Worldwide recognized clients to build skills for an excellent resume
- Top\-notch team to learn and grow with
- Competitive compensation with performance bonuses
- Work on diverse, challenging projects across industries
- Direct access to cutting\-edge tech stacks (Snowflake, Databricks, AWS, GCP, Azure)
- Builder culture where engineers lead and ship
- Professional development budget and certification support
- Flexible remote work environment
- Collaborative team that values production\-grade craftsmanship
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
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 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 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.
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