Senior AI/ML Architect

Data Ideology•Pittsburgh, PA

1d•Remote

About The Position

We are seeking a senior AI/ML Architect to join our team on a contract engagement designing the intelligence layer of an edge AI assistant system. This is a discovery, architecture, and feasibility engagement — the primary outputs are a validated AI architecture, technology assessments, and a constrained proof-of-concept demonstrator. You are not training or deploying production models in this engagement. The right candidate thinks clearly about the architecture of safe, bounded AI systems; has strong opinions about when retrieval is better than inference; and produces crisp written architecture documents that engineers can actually build from.

Requirements

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.

Nice To Haves

AWS certifications (Solutions Architect Pro or Security Specialty) are highly preferred.

Responsibilities

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. Fine-tuning or custom model training is not a default commitment in this phase — adaptation approach will be determined based on discovery findings.
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. Formal safety certification is not in scope for this engagement.
Produce all architecture recommendations as Architecture Decision Records (ADRs) with explicit trade-off rationale. Clearly distinguish confirmed decisions from those that remain conditional on hardware specifications or interface access not yet confirmed.