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About This Role
Applied AI Consultant - Contractor Position
Location: Flexible
Travel: Will depend on your location
The Opportunity
BTS is building a new kind of consulting capability: one where the consultant doesn’t just advise on AI - they build it, teach it, and help organizations get results. We’re looking for Applied AI Contractors who can sit with a client team, understand their real work, prototype an AI-powered solution in the room, and then help that team adopt it at scale.
This role bridges the gap between AI strategy (which too many firms offer) and AI engineering (which too many firms keep siloed). You’ll work across both BTS’s internal operations, where we are our own “Customer Zero” for AI adoption, and with some of the world’s largest enterprises to transform how their people work with AI every day.
This is not a “build a slide deck about AI” role. You’ll write code, run workshops, facilitate executive sessions, and ship production tools, sometimes all in the same week.
What You’ll Do
Build: Prototype to Production
- Work directly with client teams and BTS colleagues to identify high-value AI use cases grounded in real workflows, not theoretical opportunity maps
- Rapidly prototype AI-powered solutions using LLM APIs (Claude, GPT, Gemini), agentic frameworks, and integration platforms (MCP, tool-use architectures, workflow automation)
- Take prototypes through to production-grade tools deployed inside enterprise environments, working alongside client IT and data teams
- Build and maintain internal BTS AI tools and workflows as part of our Customer Zero program, dogfooding everything we take to market
- Design and implement agentic systems, MCP server integrations, RAG pipelines, and custom tooling that connects AI models to enterprise data and systems
Teach: Facilitate and Enable
- Design and deliver hands-on AI training sessions for technical audiences (engineers, data scientists, IT leaders) and commercial audiences (C-suite, sales leaders, functional heads)
- Facilitate workshops that move from “understanding AI” to “building with AI” in a single session. Participants leave with working prototypes, not just frameworks
- Develop hands-on workshops and simulations for AI adoption programs, including prompt engineering, agentic workflows, responsible AI use, and AI-augmented decision-making
- Deliver sessions to senior partners and consultants at major professional services firms (SI partners, Big Four) on behalf of BTS and our AI lab partners
- Create compelling demonstrations and interactive experiences that make abstract AI concepts tangible for non-technical leaders
Transform: Consult and Scale
- Partner with client leadership to design AI adoption strategies that connect to business outcomes: revenue growth, cost transformation, workforce productivity, and capability building
- Conduct AI readiness diagnostics and workflow assessments to identify where AI can create the highest leverage
- Develop measurement frameworks to quantify the impact of AI adoption on P&L, productivity, and capability development
- Support business development by contributing to proposals, client pitches, and go-to-market strategy for BTS’s AI adoption services
- Stay at the frontier: evaluate emerging models, tools, and architectural patterns; contribute to BTS’s point of view on applied AI
What We’re Looking For
This role lives at the intersection of building, teaching, and consulting. We know that's a rare combination, so we're not looking for someone who's already world-class at all three. We're looking for someone who's strong in at least one and has a genuine aptitude for the others.
In practice, that means we're interested in two profiles:
The Builder Who Can Consult
You've spent most of your career building things; AI-powered applications, agentic systems, workflow automations, production tools. You're fluent in Python, comfortable with LLM APIs, and you've shipped software that real people use. What makes you different from a pure engineer is that you actually enjoy being in the room with clients. You can translate what you've built into language a CFO understands. You're curious about business problems, not just technical ones. You might not have run a consulting engagement before, but you've led workshops, presented to stakeholders, or trained colleagues, and you enjoyed it!
The Consultant Who Can Build
You've spent most of your career in consulting, professional services, or client-facing technology roles. You understand how large enterprises buy, adopt, and resist change. You can design a workshop, facilitate a room of senior leaders, and write a compelling proposal. What makes you different from a pure consultant is that you're not afraid of a terminal. You've built prototypes, written code (even if imperfect), and you genuinely understand how AI systems work. Not just what they do, but how they're constructed. You don't need an engineering team to translate your ideas into something tangible.
Either way, you share these qualities:
- You move fast and learn faster. The AI landscape shifts weekly and you keep up because you're genuinely interested, not because someone told you to.
- You think in workflows, not features. You understand that AI adoption is a behavior change challenge, not just a technology deployment. You design for how people actually work.
- You're comfortable with ambiguity. This role is being built and you'll help define it. That excites you rather than unsettles you.
- You have strong opinions, loosely held. You'll advocate for an approach, but you'll change your mind when the evidence says you should.
Why BTS
BTS is not a technology consultancy that bolted on an AI practice. We are a 1,200-person global performance transformation firm that has spent 40 years helping the world’s leading companies turn strategy into results through their people. Our AI adoption work is built on this foundation: we know that technology only creates value when people change how they work.
- Work at the frontier. BTS partners directly with frontier AI labs. You’ll co-develop training materials and deliver sessions alongside AI lab teams.
- Work with extraordinary clients. Our clients include over 40 of the world’s 100 largest corporations across every major industry. You’ll work on problems that matter at genuine scale.
- Build what you sell. Our Customer Zero program means you’ll use AI to transform BTS’ own operations first, giving you battle-tested credibility with every client conversation.
- Freedom and responsibility. BTS is a flat organization where people lead projects and shape their own work from an early stage.
A Week in the Life
No two weeks look the same, but here’s a realistic snapshot:
- Monday: Facilitate a half-day AI adoption workshop for 30 senior leaders at a Fortune 500 energy company. Participants leave with three working prototypes for their highest-priority workflows.
- Tuesday–Wednesday: Build and deploy an MCP-integrated agentic tool for a client’s HR team that automates a three-hour weekly reporting process down to ten minutes.
- Thursday: Co-deliver a training session for 50 Big 4 Partners on how to position and sell AI adoption to their clients, featuring live demos for enterprise use cases.
- Friday: Internal Customer Zero sprint: improve BTS’s own Salesforce-to-Slack AI workflows, write a LinkedIn post on a pattern you’ve noticed across client engagements, and contribute to a proposal for a new client pursuit.
#LI-Remote
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 9,653 AI roles we're tracking, AI Consultant positions make up 0% of the market. At BTS, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Consultant roles pay a median of $148,848 based on 19 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.
BTS AI Hiring
BTS has 1 open AI role right now. They're hiring across AI Consultant. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
Career Path
Common paths into AI Consultant roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
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).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
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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