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About This Role
Robots \& Pencils is seeking a seasoned AWS AI Solutions Architect to lead the design and delivery of complex, enterprise\-grade generative and agentic AI systems built on Amazon Web Services. You will architect scalable, secure, and production\-ready AI platforms leveraging Amazon Bedrock, Amazon Bedrock AgentCore, AWS Strands Agents, AWS AgentCore Gateway, Nova Forge, Nova 2 Sonic, and related AWS AI/ML services.
As an AWS AI Solutions Architect, you will serve as a strategic technical advisor—translating ambiguity into structured AWS\-native architectures, validating designs through hands\-on prototyping, and ensuring every solution aligns with the AWS Well\-Architected Framework (including ML Lens) while delivering measurable business value.
Key Responsibilities
Client Engagement \& AWS Solutions Architecture
- Serve as the primary AWS AI architecture partner for strategic clients, driving generative and agentic AI system design from discovery through production.
- Lead architecture design using Amazon Bedrock (including foundation models and custom models), Bedrock AgentCore, AWS Strands Agents, and AWS AgentCore Gateway.
- Design advanced RAG, Agentic RAG, and multi\-agent orchestration architectures leveraging AWS\-native services such as Lambda, Step Functions, API Gateway, DynamoDB, Aurora (pgvector), and OpenSearch.
- Produce AWS reference architectures, architecture decision records (ADRs), and implementation roadmaps aligned to business objectives.
- Validate feasibility through hands\-on prototyping in Python using Bedrock SDKs, SageMaker, and serverless services.
- Ensure architectures follow AWS security best practices (IAM, KMS, VPC, PrivateLink) and cost optimization principles.
Outcome Ownership \& Business Impact
- Own architectural integrity from concept through production deployment on AWS.
- Align solutions with AWS Well\-Architected Framework pillars: Operational Excellence, Security, Reliability, Performance Efficiency, Cost Optimization, and Sustainability.
- Guide clients through tradeoff decisions across model selection (Bedrock FMs vs custom SageMaker models), latency, cost, governance, and compliance.
Accelerate time\-to\-value through reusable AWS accelerators, Infrastructure as Code (CloudFormation/Terraform/CDK), and CI/CD automation.
- Continuously evaluate emerging AWS AI capabilities (Nova Forge, Nova 2 Sonic, Bedrock updates, and new AgentCore capabilities).
Engineering Leadership \& Delivery Excellence
- Provide architectural oversight to Forward Deployed Engineers and AWS delivery teams.
- Establish best practices for MLOps on AWS including model lifecycle management, monitoring, and observability using SageMaker, CloudWatch, CloudTrail, and AWS Config.
- Define governance, responsible AI guardrails, Bedrock Guardrails configuration, and security controls for enterprise environments.
- Mentor engineers on AWS AI service integration, distributed systems design, and secure multi\-account strategies.
- Make principled tradeoffs under constraints related to privacy, compliance (SOC2, HIPAA, GDPR), cost, and operational complexity.
Cross\-Functional Collaboration
- Partner with internal product, engineering, research, and customer success teams to evolve AWS\-based AI offerings.
- Contribute AWS reference architectures and reusable infrastructure modules to internal accelerators.
- Support pre\-sales engagements including architecture workshops, AWS migration strategy, and solution scoping.
- Collaborate across distributed teams and client stakeholders across North America.
Required Skills \& Qualifications
- Bachelor's degree in Computer Science, Engineering, or equivalent experience.
- 10\+ years of experience in software engineering or cloud architecture with deep AWS ownership.
- Deep expertise in Amazon Bedrock, Bedrock AgentCore, AWS Strands Agents, AgentCore Gateway, and related AWS AI services.
- Strong familiarity with SageMaker (training, deployment, pipelines), deep learning fundamentals, and model fine\-tuning strategies.
- Experience architecting RAG, multi\-agent, and orchestration systems using AWS\-native services.
- Strong knowledge of distributed systems, event\-driven architectures, and serverless patterns.
- Proficiency with Infrastructure as Code (AWS CDK, CloudFormation, Terraform).
- Hands\-on development capability in Python and AWS SDKs.
- Experience implementing observability and monitoring strategies in AWS environments.
- Proven success leading enterprise\-scale AWS transformations.
- Exceptional communication skills for both technical and executive audiences.
- AWS Professional Certifications highly preferred (AWS Solutions Architect – Professional, AWS DevOps Engineer – Professional).
Nice to Have
- AWS Specialty certifications (Machine Learning – Specialty, Security – Specialty).
- Experience with advanced agentic reasoning patterns (ReAct,CoT, Tree\-of\-Thoughts) implemented on Bedrock.
- Experience building secure multi\-account AWS organizations using Control Tower.
- Exposure to data engineering services such as Glue, Redshift, Lake Formation.
- Consulting or professional services background.
Personal Competencies
- Accountability – Owns AWS architectural direction and client outcomes with rigor.
- Adaptability – Rapidly adopts new AWS AI releases and evolving generative AI capabilities.
- Collaboration – Builds trust across engineering and executive stakeholders.
- Execution\-Focused – Balances innovation with production\-ready AWS delivery.
- Innovation\-Minded – Experiments responsibly with emerging AWS AI services.
- Craftsmanship – Designs secure, scalable, and well\-documented AWS systems.
- Leadership with Courage – Drives architectural alignment in complex environments.
- Comfort in Ambiguity – Translates unclear AI requirements into AWS\-native solution architectures.
Why Join Robots \& Pencils?
We build smart systems for a human world — blending creativity, engineering, and AWS\-powered AI to help organizations reimagine how they work. As an AWS AI Solutions Architect, you will shape enterprise\-scale generative and agentic AI platforms using the most advanced AWS services available. You will define architectures that deliver measurable business value, mentor teams, and directly influence the evolution of our AWS AI practice.
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 3,708 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 $218,750 based on 3,817 positions with disclosed compensation.
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.
Robots & Pencils AI Hiring
Robots & Pencils has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Seattle, WA, US.
Location Context
AI roles in Seattle pay a median of $236,900 across 267 tracked positions. That's 9% above the national median.
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 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).
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 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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