Senior AI/ML Architect
Data Ideology
At 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
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
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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Produce all architecture recommendations as Architecture Decision Records (ADRs) with explicit trade-off rationale. Clearly distinguish confirmed decisions from thos