Senior AI/ML Architect

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. For more information about Data Ideology, visit www.dataideology.com

Key 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.

Supervisory Responsibilities: None

Qualifications

*Education and Experience:*

• 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.

Work Environment:

• Remote work from home.
• Hours of work and days are generally Monday through Friday. Specific business hours will depend on client needs.

Physical Demands:

• Must be able to remain in a stationary position 50% of the time.
• The person in this position must occasionally move about inside the office to access file cabinets, library stacks, office machinery, etc.
• Constantly operates a computer and other office productivity machinery, such as a calculator, copy machine, and printer.
• The person in this position frequently communicates with clients and coworkers. Must be able to exchange accurate information in these situations.

Data Ideology is an EEO Employer

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