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About This Role
AI Architect / AI Subject Matter Expert
About TechTorch
TechTorch is a high\-growth enterprise technology consultancy partnering with leading private equity\-backed businesses to accelerate value creation through AI, automation, and digital transformation. We help clients define AI strategy, architect enterprise\-grade AI solutions, and deliver measurable business outcomes.
About the AI Practice
The AI Practice helps organizations move beyond AI experimentation into production. We advise executive stakeholders, design enterprise AI architectures, develop reusable AI offerings, and accelerate delivery through modern AI technologies.
About the Role
We are looking for a AI Architect / AI Subject Matter Expert to lead strategic AI engagements for enterprise clients. This is a client\-facing consulting role combining AI strategy, enterprise architecture, deep technical expertise, and thought leadership. You will help shape both client AI roadmaps and TechTorch's AI offerings.
What You'll Do
- Lead AI strategy, discovery workshops, and executive advisory sessions.
- Assess AI maturity, business processes, and identify high\-value AI opportunities.
- Design enterprise AI architectures and implementation roadmaps.
- Define AI operating models, governance, security, and Responsible AI practices.
- Architect solutions using LLMs, RAG, Agentic AI, MCP, vector databases, and enterprise AI platforms.
- Partner with engineering teams to guide implementation and review solution designs.
- Shape TechTorch AI offerings, accelerators, playbooks, and reference architectures.
- Support proposals, pre\-sales, and executive presentations.
- Mentor consultants and AI engineers while promoting best practices.
Required Experience
- 8\+ years in AI, enterprise architecture, solution architecture, or software engineering.
- Proven experience designing production AI solutions.
- Strong consulting, stakeholder management, and executive communication skills.
- Experience leading AI discovery workshops and roadmap definition.
- Ability to translate business challenges into scalable AI solutions.
Technical Expertise
- Generative AI: OpenAI, Claude, Gemini, Azure OpenAI
- AI Architecture: RAG, AI Agents, Agentic AI, MCP, Prompt Engineering, Vector Databases, AI Evaluation, AI Governance
- Frameworks: LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, FastAPI
- Cloud: Azure AI, AWS Bedrock, Google Vertex AI
- Developer Productivity: Claude Code, Cursor, GitHub Copilot, CI/CD
Success Metrics
- Successful delivery of enterprise AI strategies and architectures.
- Adoption of AI roadmaps by executive stakeholders.
- Creation of reusable AI accelerators and TechTorch IP.
- Measurable client business outcomes.
- Technical leadership across AI Practice engagements.
Our Values
Our values reflect the DNA of the private equity\-backed companies we serve — focused on speed, ownership, accountability, and results:
- Client First – We focus relentlessly on delivering outcomes that create value for our clients
- We, Not Me – We win together. Collaboration drives transformation at scale
- Get Stuff Done – We execute with speed and precision — because in PE, time matters
- AI First – We embed AI at the core, enabling scalable, high\-leverage solutions
- Own It – We take accountability for results, delivering on what we promise
- Agile Mindset – We adapt quickly and proactively seek better ways to move forward
What We Offer
- Flexible, remote\-first work environment with high\-performance expectations and autonomy.
- Semi\-annual team off\-sites — we come together in person at least twice a year to connect, recharge, and do the work that's better face\-to\-face.
- A team that takes AI tooling seriously and expects you to integrate it into your work.
- High\-autonomy, high\-ownership work across the full arc of real client problems.
- Access to the full modern data and AI stack — no one\-tool shops.
- Exposure to top\-tier private equity firms and their portfolio companies.
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 4,317 AI roles we're tracking, AI Architect positions make up 1% of the market. At Techtorch, 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 Architect roles pay a median of $237,300 based on 102 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Techtorch AI Hiring
Techtorch has 1 open AI role right now. They're hiring across AI Architect. Based in US.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
Career Path
Common paths into AI Architect 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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