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About This Role
KL Software Technologies (“KLST”) iis hiring a hands\-on AI Architect to lead our transformation into an agentic\-AI\-first product engineering organization. You will design, deploy, and operate multiple fleets of autonomous AI agents, powered by Anthropic Claude (Claude Code) and/or OpenAI Codex, that build, test, and ship software features, run product management workflows, and execute digital marketing campaigns across our flagship KLST products (learn more here www.klstinc.com/whyklstforlegal).
This is NOT a chatbot or data\-science role. You will industrialize software delivery using agentic AI developer tools – Claude Code, AI code review and optimization tools (e.g., ponytail), multi\-agent product build orchestrators (e.g., gstack, which spins up CEO / PM / BA / QA / Dev agents that work together to deliver a complete product), and free / lower\-cost open\-source options such as NVIDIA’s agentic AI toolkits (NeMo Agent Toolkit and NIM microservices).
The Mission
Stand up and operate AT LEAST FIFTY (50\) 24/7 autonomous coding and QA agents delivering product features across KLST products by the end of the year – organized so that each Senior Engineer owns and manages 5–10 agents, reviews every agent’s output, and validates results BEFORE any code is allowed to be committed. In parallel, stand up autonomous product management and digital marketing agents that plan, prioritize, and promote the same product roadmap.
Key Responsibilities:
- Architect, deploy, and scale a multi\-agent software delivery platform using Claude Code, agentic code review/optimization tools (ponytail or similar), product build orchestrators (gstack or similar), and NVIDIA’s open\-source agentic toolkits – selecting the right mix of commercial and free/open\-source tooling to control cost.
- Design end\-to\-end agent workflows covering the full SDLC – requirements, design, coding, code review, QA automation, and release – with human\-in\-the\-loop approval gates at every commit.
- Build coding agents that autonomously pull work items/tickets from Azure DevOps Boards, generate implementation code with Claude Code and/or OpenAI Codex to meet each ticket’s requirements, self\-QA the code against positive and negative test cases, and check the validated code into the repository.
- Build product management agents that autonomously groom the backlog, draft and refine requirements and user stories, prioritize work, and generate release notes and status reporting.
- Build digital marketing agents that autonomously plan, generate, and optimize marketing content and campaigns – SEO, email, social, and web – tied to product launches and releases.
- Define and enforce the agent governance model: mandatory Senior Engineer review and validation of agent output before commits, branch protection, audit trails, rollback, and security / IP safeguards for AI\-generated code.
- Enable and coach Senior Engineers to become “agent managers”, each owning and supervising 5–10 agents; build the playbooks, prompt libraries, guardrails, and evaluation metrics they use to review and validate agent output.
- Integrate the agent fleet with our Git / CI\-CD pipelines (Azure DevOps / GitHub) so agents work 24/7 within guardrails across netDocShare, imDocShare, and KLapper repositories.
- Continuously measure and report agent fleet productivity, code quality, defect escape rates, and cost per feature; optimize model/toolkit selection (Claude vs. open source / NVIDIA) for cost and performance.
- Stay current with the agentic AI ecosystem (Model Context Protocol, multi\-agent orchestration, agent evaluation frameworks) and continuously upgrade KLST’s Agentic Delivery Platform.
Key Qualifications:
Required Skills
- Very strong, demonstrable background setting up and operating MULTIPLE autonomous AI agents in production – designing agent architectures, orchestrating agent\-to\-agent collaboration, and running fleets of agents 24/7 with reliability and guardrails.
- Overall, at least TEN (10\) years “hands\-on” software engineering experience with a strong full\-stack background (.NET / TypeScript / React or Angular / REST APIs / SQL) on Azure or AWS.
- Minimum TWO (2\) years of hands\-on experience building with LLMs and agentic AI developer tools – Claude Code and/or OpenAI Codex (or GitHub Copilot / Cursor / Windsurf), prompt engineering, and LLM APIs (Anthropic, OpenAI, Google, or open\-weight models).
- Hands\-on experience with multi\-agent orchestration frameworks and toolkits – e.g., gstack, ponytail, NVIDIA NeMo Agent Toolkit, LangGraph, AutoGen, or CrewAI – including agent\-to\-agent workflows (PM / BA / Dev / QA agent roles).
- Hands\-on experience building autonomous coding agents that ingest tickets / work items from Azure DevOps, generate code with Claude Code and/or OpenAI Codex to meet the requirements, validate it against positive and negative test cases, and commit the code – end\-to\-end with minimal human intervention.
- Experience building autonomous agents beyond software delivery – product management agents (backlog grooming, requirements / user\-story generation, prioritization, reporting) and digital marketing agents (content generation, campaign planning, SEO / email / social execution).
- Strong experience with automated code review and QA automation – unit / integration / end\-to\-end testing (Playwright, Selenium, or similar) and using AI agents to author and execute test suites – including both positive and negative test cases – before code is committed.
- Strong DevOps skills: Git branching and PR workflows, CI/CD (Azure DevOps or GitHub Actions), containerization, and secrets/access management for autonomous agents.
- Proven ability to define engineering governance for AI\-generated code: review gates, quality metrics, traceability, and compliance controls.
- Strong presentation and communication skills (both written and verbal) are required; able to train and influence senior engineers to adopt the agent\-manager operating model.
Preferred Skills
- Experience with Model Context Protocol (MCP) servers, RAG pipelines, and agent evaluation / benchmarking frameworks.
- Experience building product management agents with tools such as Azure DevOps Boards, Jira, or Aha! automating backlog grooming, roadmap updates, and stakeholder reporting.
- Experience building digital marketing agents across SEO, content / CMS, email automation, and social platforms – connecting marketing workflows to product launches and releases.
- Knowledge of the Microsoft 365 / SharePoint ecosystem and legal document management platforms (iManage, NetDocuments), the domain of netDocShare and imDocShare.
- Experience optimizing LLM spend, prompt caching, model routing, and running open\-weight models on NVIDIA GPUs as a lower\-cost alternative to commercial APIs.
- Microsoft Azure, AWS, or NVIDIA certifications.
- Willing to travel nationally or internationally on temporary and permanent assignments (United States, Australia, India).
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 3,708 AI roles we're tracking, AI Architect positions make up 1% of the market. At KL Software Technologies, Inc., 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 $254,798 based on 67 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
KL Software Technologies, Inc. AI Hiring
KL Software Technologies, Inc. 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 87 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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