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About This Role
Lead Architect \- AI\- with Nvidia
Location: Remote USA
Mode of Hire: Fulltime
We are building a next\-generation Vision AI software platform designed specifically for US\-based public sector clients — spanning federal agencies, defense contractors, smart\-city programs, and critical infrastructure operators. As our Lead Architect / Head of Architecture, you will be the technical authority shaping every layer of the platform: from constrained edge devices streaming sensor data via MQTT, through GPU\-accelerated inference running on NVIDIA hardware, to the cloud\-side orchestration and data pipelines that give our clients actionable intelligence.
You will work at the intersection of systems design, security engineering, and product strategy, translating complex government requirements into elegant, scalable architecture — and then ensuring a globally distributed engineering team executes against that vision with coherence and velocity.
Key Responsibilities
Platform Architecture \& Technical Leadership
- Own the end\-to\-end architecture of the Edge AI / IoT platform, defining reference designs, component boundaries, data flows, and integration contracts across edge, on\-premises, and cloud tiers.
- Drive adoption of open standards (MQTT, VSS, OPC\-UA, ONVIF, etc.) and NVIDIA ecosystem tooling (CUDA, TensorRT, Jetson, NGC) across the product.
- Establish and govern architecture decision records (ADRs), technology radar, and technical roadmap in close collaboration with product management and the CTO.
- Identify and mitigate systemic technical risks — latency, bandwidth constraints, model drift, security boundaries — before they become production incidents.
Security \& Compliance
- Lead the architecture design to achieve and maintain NIST SP 800\-171 compliance, with a clear pathway toward CMMC Level 2 (and Level 3 where required by contracts).
- Define zero\-trust principles, secure\-by\-design patterns, cryptographic key management, and supply chain security practices for all platform components.
- Collaborate with our compliance and legal teams to produce architecture documentation, system security plans (SSPs), and evidence packages required during government procurement.
Engineering Excellence \& Team Enablement
- Serve as the primary technical mentor for a globally distributed engineering team spanning multiple time zones; run architecture reviews, design sprints, and internal tech talks.
- Define and enforce engineering standards: API design, containerization and orchestration (Kubernetes), CI/CD pipeline quality gates, observability, and disaster recovery.
- Partner with engineering leads to decompose the architecture into actionable epics and ensure architectural intent is preserved through implementation.
Client \& Stakeholder Engagement
- Act as the senior technical point of contact during pre\-sales and onboarding with US public sector clients, translating customer mission requirements into platform capabilities.
- Participate in government\-facing technical evaluations, RFP responses, and architecture briefings — demonstrating deep understanding of federal IT environments (FedRAMP\-adjacent infrastructure, air\-gapped deployments, etc.).
- Maintain trusted relationships with technology partners including NVIDIA, cloud providers, and standards bodies relevant to the IoT / edge AI space.
Required Qualifications
- 10\+ years of software engineering experience, with at least 4 years in a principal architect, staff engineer, or equivalent technical leadership role.
- Proven track record designing large\-scale distributed systems with real\-time or near\-real\-time constraints — IoT telemetry pipelines, sensor fusion, or similar.
- Deep expertise with MQTT (broker architecture, QoS, retained messages, MQTT over TLS) and at least one complementary messaging/streaming technology (Kafka, AMQP, ROS 2, etc.).
- Hands\-on experience with the NVIDIA ecosystem: CUDA development, TensorRT optimization, Jetson or similar edge AI hardware, and NVIDIA NGC / Triton Inference Server.
- Solid understanding of Vehicle Signal Specification (VSS) or comparable hierarchical signal / data modeling standards for edge device abstraction.
- Practical knowledge of NIST SP 800\-171 controls and experience translating them into concrete architecture and engineering requirements.
- Proficiency in containerized, cloud\-native architectures: Kubernetes, Helm, service mesh, GitOps, and modern observability stacks (OpenTelemetry, Prometheus, Grafana).
- Exceptional written and verbal communication skills — able to produce crisp architecture diagrams and documents for both engineering peers and non\-technical government stakeholders.
- Ability to work effectively across globally distributed teams, exercising influence without direct authority.
Nice to Have
- Experience with CMMC Level 2/3 assessments or FedRAMP authorization processes.
- Familiarity with additional edge / embedded protocols: OPC\-UA, DDS, Matter, LoRaWAN, or TSN.
- Background in one or more US public sector verticals: defense, smart cities, utilities / SCADA, transportation, or emergency services.
- Contributions to open\-source projects in the IoT, edge AI, or cloud\-native space.
- Experience with software\-defined networking, 5G private networks, or network slicing for edge deployments.
- Domain\-specific certifications like GCP, TOGAF, Open Group, etc.
- US Person status (useful for certain client engagements, not a hard requirement).
What We Offer
- A mission\-driven role with direct impact on critical infrastructure and public services across the United States.
- Full autonomy to shape a greenfield platform architecture from the ground up.
- Competitive compensation package including base salary, equity, and performance bonus — benchmarked globally.
- Flexible, fully remote work environment with async\-first culture and periodic team on\-sites.
- Access to cutting\-edge NVIDIA GPU infrastructure for research and development.
- Dedicated learning \& conference budget, and active participation in industry standards bodies.
Job Type: Full\-time
Pay: $150,000\.00 \- $155,000\.00 per year
Benefits:
- Health insurance
Work Location: Remote
Salary Context
This $150K-$155K range is below the median for AI Architect roles in our dataset (median: $169K across 31 roles with salary data).
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 3,823 AI roles we're tracking, AI Architect positions make up 1% of the market. At Freelancing, 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 $212,500 based on 108 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($152K) sits 28% below the category median. Disclosed range: $150K to $155K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Freelancing AI Hiring
Freelancing has 2 open AI roles right now. They're hiring across AI Architect, AI/ML Engineer. Based in Remote, US. Compensation range: $145K - $155K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% of all AI roles offer remote work.
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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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