Senior AI/ML Architect

May 30, 2026

Data IdeologyAt DI, we provide Data & Analytics expertise to drive measurable business outcomes, often solving complex business problems for our clients. Our data analytics advisory services enable our customers to transform data into insights by driving a culture of empowerment and ownership of results. Our team consists of highly motivated individuals passionate about learning, understanding, collaborating, and intellectually curious. For more information about Data Ideology, visit <a href="http://www.dataideology.com/">www.dataideology.com</a> Senior AI/ML Architect - (Contract 1099)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 <ul><li>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. </li></ul><ul><li>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. </li></ul><ul><li>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. </li></ul><ul><li>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. </li></ul><ul><li>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. </li></ul><ul><li>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. </li></ul><ul><li>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. </li></ul><ul><li>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. </li></ul><ul><li>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. </li></ul><ul><li>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. </li></ul>Supervisory Responsibilities: NoneQualificationsEducation and Experience:<ul><li class="MsoNoSpacing" style="margin-left:8px;">Bachelor’s degree in Computer Science, Engineering, or equivalent professional experience; AWS certifications (Solutions Architect Pro or Security Specialty) are highly preferred.</li><li class="MsoNoSpacing" style="margin-left:8px;">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.</li><li class="MsoNoSpacing" style="margin-left:8px;">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.</li><li class="MsoNoSpacing" style="margin-left:8px;">Deep experience building and maintaining CI/CD pipelines for infrastructure, including environment promotion (Dev/Stage/Prod), drift detection, and automated validation.</li><li class="MsoNoSpacing" style="margin-left:8px;">Solid networking fundamentals, including VPC design, PrivateLink, and identity federation patterns (SAML/OAuth2/mTLS).</li><li class="MsoNoSpacing" style="margin-left:8px;">Demonstrated ability to design airtight data isolation at scale (ABAC/RBAC) and produce builder-ready technical standards such as Architecture Decision Records (ADRs).</li><li class="MsoNoSpacing" style="margin-left:8px;">Strong financial acumen with the ability to track AWS spend against cost models and drive optimization through resource tagging and architectural efficiency.</li></ul>Work Environment: <ul><li>Remote work from home. </li><li>Hours of work and days are generally Monday through Friday. Specific business hours will depend on client needs. </li></ul>Physical Demands: <ul><li>Must be able to remain in a stationary position 50% of the time. </li><li>The person in this position must occasionally move about inside the office to access file cabinets, library stacks, office machinery, etc. </li><li>Constantly operates a computer and other office productivity machinery, such as a calculator, copy machine, and printer. </li><li>The person in this position frequently communicates with clients and coworkers. Must be able to exchange accurate information in these situations. </li></ul>Data Ideology is an EEO Employer

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