robotsandpencils

Principal AI Engineering Architect

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At a Glance

Location
United States
Work Regime
remote
Experience
8+ years
Compensation
is a plus Our salary range is $180,375 – $230,625 USD
Posted
2026-07-29T10:17:12-04:00

Key Requirements

Required Skills

AWSAzureCI/CDDockerETLGCPKafkaKubernetesMicroservicesMongoDBPostgreSQLPyTorchPythonSnowflakeTensorFlowTerraform

Certifications

  • TOGAF

Domain Knowledge

  • Engineering
  • Regulatory

Requirements

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)

Responsibilities

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)