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About This Role
Principal AI Engineering Architect
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We're looking for a Principal AI Engineering Architect to lead the design and delivery of complex, multi\-domain systems spanning cloud, data, and AI — with deep, hands\-on mastery of multi\-agent agentic AI solutions. This role is ideal for a deeply experienced engineer who owns the hardest architectural challenges, sets technical direction, and serves as the senior technical voice on the engagements they support, while also being able to roll up their sleeves and lead model development, agentic system design, and production delivery end\-to\-end.
In this role, you will operate as the senior technical authority on a cross\-functional team, defining architecture across cloud infrastructure, data platforms, and AI/ML workloads, with a strong bias toward AWS\-native services and AWS GenAI offerings. You'll partner closely with leadership and clients on technical strategy, lead the design and delivery of complex, production\-grade multi\-agent systems, mentor experienced engineers, and own high\-stakes decisions that shape the long\-term success of the systems you build.
Why This Role Matters
At Robots \& Pencils, we design AI systems for a human world. Our name says it all. Robots and pencils means engineering paired with creativity, because every agent we ship has to work for real people in real workflows. That balance is baked into how we operate.
Every role here contributes directly to that mission. Here, you shape how AI systems integrate into enterprise operations, how teams move at real velocity, and how products create measurable impact for clients and the people they serve. We ship production\-ready AI in 30 to 45 days. That pace demands people who take ownership, lead with craft, and care deeply about what they put their name on.
What You'll Do
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Craft \& Delivery
Define technical strategy and lead architectural design across cloud, data, and AI/ML systems for end\-to\-end engagements, owning architecture decisions and driving solutions from research through production at scale
Architect and ship production\-grade multi\-agent agentic AI systems, including agent orchestration, tool use, memory, and inter\-agent communication patterns
Design and build with Amazon Bedrock AgentCore and complementary AWS GenAI services to deploy, scale, and operate agentic workloads securely in production
Architect scalable cloud\-native solutions with a strong bias toward AWS, including multi\-cloud and hybrid strategies where needed (AWS primary, with Azure, GCP, Kubernetes as secondary)
Design data architectures including warehouses, data lakes, and pipelines for batch and streaming workloads (e.g., Snowflake, Redshift, BigQuery, Spark, Kafka)
Design AI/ML systems including model serving, MLOps pipelines, feature stores, and LLM\-based applications (e.g., SageMaker, Bedrock, AgentCore, Vertex AI, MLflow, Hugging Face)
Build and evolve scalable ML platforms, pipelines, and infrastructure that support reliable, repeatable model development and deployment across teams
Define infrastructure as code, CI/CD, and DevOps standards across engagements (e.g., Terraform, CloudFormation, GitHub Actions)
Drive performance, scalability, cost, and reliability optimization across deployed systems
Ensure architecture meets security, governance, and compliance requirements (e.g., GDPR, HIPAA, SOC2\)
Lead cloud migrations and platform modernization initiatives
Set the standard for AI\-forward engineering, using tools like Claude and Cursor with sophistication and helping the team adopt them effectively
Collaboration \& Communication
Partner with senior leadership and clients as the principal technical voice on strategy and direction
Translate complex AI tradeoffs, risks, and opportunities into clear narratives that drive decision\-making across technical and non\-technical stakeholders
Lead design reviews and technical discussions, raising the bar for engineering rigor and constructive challenge across the team
Engage closely with engineering, data, AI, and product teams to align architecture with broader business priorities
Develop and maintain architecture documentation, standards, and guidelines
Leadership \& Influence
Define and champion architectural standards and best practices across the engagements you support, bringing depth on tradeoffs, long\-term implications, and responsible AI practices
Mentor and grow engineers at all levels, multiplying impact through coaching, code reviews, and pairing on hard problems
Own the most difficult architectural, integration, and agentic\-system challenges, serving as the senior technical decision\-maker and driving them through to production with care for reliability, cost, and safety
Evaluate emerging technologies — especially in the agentic AI and AWS ecosystems — and recommend tools, frameworks, and patterns that improve architecture over time
What You'll Bring
8\+ years of software engineering experience, with at least 5 years in technical leadership roles and 4\+ years focused on AI/ML systems in productionExpert software engineering background (Python or similar) with strong design sensibilities for scalable, maintainable systems
Deep, hands\-on expertise designing and shipping production multi\-agent agentic AI systems, including agent orchestration, planning, tool use, and multi\-agent coordination patterns
Deep expertise with AWS, including in\-depth knowledge of AWS GenAI offerings and hands\-on experience with Amazon Bedrock AgentCore; broader multi\-cloud experience (Azure, GCP) is a plus
Strong background in microservices, serverless, containers, and event\-driven systems (e.g., Kubernetes, Docker, Lambda, EventBridge)
Proficiency with infrastructure as code and CI/CD (e.g., Terraform, CloudFormation, Pulumi, GitHub Actions)
Strong data architecture expertise across relational, NoSQL, and big data systems (e.g., PostgreSQL, MongoDB, Snowflake, BigQuery, Spark, Kafka)
Hands\-on experience with data modeling, ETL/ELT pipelines, and orchestration (e.g., Airflow, Prefect, dbt)
Mastery of AI frameworks and orchestration tools for building agentic systems (e.g., LangChain, LangGraph, AgentCore, CrewAI, AutoGen, or equivalents)
Strong experience designing AI/ML systems for production, including LLMs, MLOps, and model serving (e.g., SageMaker, Bedrock, Vertex AI, MLflow, Hugging Face, PyTorch, TensorFlow)
Strong experience with evaluation frameworks and observability tools for LLM and agentic apps, including building these capabilities where they don't yet exist
Deep understanding of AI safety, responsible AI principles, prompt injection defenses, and PII handling
Extensive experience building RAG pipelines: chunking strategies, embedding models, vector databases, and advanced retrieval techniques
API design experience, including architecting and integrating with internal and third\-party services at scale
Advanced cost optimization expertise: token economics, caching strategies, model routing, quantization
Solid understanding of networking, security, identity, and access management in cloud environments
Experience with governance, compliance, and observability frameworks
Track record of senior technical leadership and mentoring experienced engineers
Strong stakeholder communication skills, with the ability to translate technical depth across audiences
Demonstrable, day\-to\-day usage and expert knowledge of AI\-forward coding tools such as Claude Code and Cursor
Multi\-cloud architecture experience, AI ethics or responsible AI experience, or enterprise architecture certifications (e.g., TOGAF, AWS/Azure/GCP) is a plus
Our salary range is $180,375 – $230,625 USD
Salary Context
This $180K-$230K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Robots & Pencils, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $180K to $230K.
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.
Robots & Pencils AI Hiring
Robots & Pencils has 4 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Positions span Seattle, WA, US, Remote, US. Compensation range: $209K - $230K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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 4,317 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).
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 4,317 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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