Principal AI Engineering Architect
Robots and Pencils · Remote · US Remote · $180.4k–$230.6k
Posted by Robots and Pencils on their careers site · View the original posting
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Mid level
Open 101 days
Hiring? Uncertain · Open 101 days.
The posting, as Robots and Pencils wrote it
Principal AI Engineering Architect
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
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 develo
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s, 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