Senior AI/ML Architect

Data Ideology, LLC

Senior AI/ML Architect designing the intelligence layer of an edge AI assistant system for Data Ideology. Leading SLM candidate evaluation, architecture design, and collaboration with AWS Solutions Architect.

Posted 7/11/2026contractRemote • 🇺🇸 United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in Cloud Infrastructure and Platform Engineering, with a focus on AWS services and architecture design. Capable of leading multi-tenant data platforms and ensuring compliance through robust safety design principles and operational boundaries.

Highest-signal resume keywords

AWS Core Services ExpertiseTerraform Module AuthoringCI/CD Pipeline DevelopmentData Isolation DesignCloud Infrastructure Leadership

ATS Keywords

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Hard Skills

Cloud InfrastructurePlatform EngineeringAWS S3AWS GlueAWS RedshiftAWS Lake FormationAWS IoT CoreAWS KMSTerraformCI/CD Pipelines

Soft Skills

CollaborationDocumentationFinancial Acumen

Tools & Technologies

AWSTerraformVPC DesignPrivateLinkSAMLOAuth2MTLS

Certifications & Qualifications

Bachelor’s Degree in Computer ScienceAWS Solutions Architect ProAWS Security Specialty

Industry Keywords

Multi-Tenant ArchitecturesEvent-Driven ArchitecturesArchitecture Decision RecordsData IsolationOperational Boundaries

Tech Stack

Tools & technologies

Amazon RedshiftAWSCloudIoTTerraform

About the role

Key responsibilities & impact

Lead SLM candidate evaluation and selection: assess Small Language Model options for edge deployment against hardware constraints, inference latency requirements, domain restriction feasibility, and licensing.
Produce a technology assessment with explicit trade-off rationale and a recommended approach.
Design the domain restriction and guardrails architecture: define how the SLM is constrained to a known operational scope, how out-of-domain responses are prevented, and how the system enforces retrieval-first, non-authoritative behavior appropriate for a safety-adjacent environment.
Design the capability framework that structures how the system responds to operator queries — how capabilities are scoped and isolated, how the framework supports incremental addition of new interaction types over time, and what the prototype will implement.
Design the retrieval-augmented inference pipeline: define how the SLM retrieves context from a local knowledge store at inference time, including retrieval strategy, context injection approach, and latency budget appropriate for the edge environment.
Evaluate candidate cloud services for knowledge retrieval, model governance, and fleet-level model lifecycle management including over-the-air model distribution to edge devices.
Produce architecture recommendations aligned to client enterprise standards; all service selections are subject to client review and approval.
Define the offboard ML lifecycle: how models are evaluated, adapted through prompting and retrieval augmentation, versioned, governed, and distributed at scale.
Collaborate with the Edge ML / Embedded Engineer on hardware constraint inputs that shape SLM selection and inference pipeline design, ensuring architecture recommendations are grounded in confirmed runtime feasibility.
Collaborate with the AWS Solutions Architect on candidate cloud service architecture for model governance, knowledge retrieval, and the model update pipeline, ensuring the cloud-side AI architecture aligns with the broader platform.
Document safety design principles and operational boundaries — authority separation, bounded AI behavior, explainability approach, and human-in-the-loop considerations — as architecture artifacts for client engineering and compliance review.
Produce all architecture recommendations as Architecture Decision Records (ADRs) with explicit trade-off rationale.

Requirements

What you’ll need

Bachelor’s degree in Computer Science, Engineering, or equivalent professional experience; AWS certifications (Solutions Architect Pro or Security Specialty) are highly preferred.
7+ years of experience in Cloud Infrastructure or Platform Engineering, with a proven track record of leading multi-tenant AWS data platforms and event-driven architectures.
Expert-level hands-on proficiency with AWS core services (S3, Glue, Redshift, Lake Formation, IoT Core, KMS) and authoring complex Terraform modules with remote state management.
Deep experience building and maintaining CI/CD pipelines for infrastructure, including environment promotion (Dev/Stage/Prod), drift detection, and automated validation.
Solid networking fundamentals, including VPC design, PrivateLink, and identity federation patterns (SAML/OAuth2/mTLS).
Demonstrated ability to design airtight data isolation at scale (ABAC/RBAC) and produce builder-ready technical standards such as Architecture Decision Records (ADRs).
Strong financial acumen with the ability to track AWS spend against cost models and drive optimization through resource tagging and architectural efficiency.

Benefits

Comp & perks

Remote work from home.
Monday through Friday work hours.
Specific business hours will depend on client needs.