{"schemaVersion":"jobsearcher.job.v1","id":"00b7f76c5bbda43df2bc6bc2","url":"https://jobsearcher.com/jobs/00b7f76c5bbda43df2bc6bc2","canonicalUrl":"https://jobsearcher.com/jobs/00b7f76c5bbda43df2bc6bc2","title":"Senior AI/ML Architect","description":"Data Ideology\nAt 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 www.dataideology.com\nSenior AI/ML Architect - (Contract 1099)\nWe 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\nKey Responsibilities\nLead 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.\nDesign 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.\nDesign 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.\nDesign 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.\nEvaluate 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.\nDefine 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.\nCollaborate 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.\nCollaborate 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.\nDocument 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.\nProduce 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.\nSupervisory Responsibilities: None\nQualifications\nEducation and Experience:\nBachelor’s degree in Computer Science, Engineering, or equivalent professional experience; AWS certifications (Solutions Architect Pro or Security Specialty) are highly preferred.\n7+ 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.\nExpert-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.\nDeep experience building and maintaining CI/CD pipelines for infrastructure, including environment promotion (Dev/Stage/Prod), drift detection, and automated validation.\nSolid networking fundamentals, including VPC design, PrivateLink, and identity federation patterns (SAML/OAuth2/mTLS).\nDemonstrated ability to design airtight data isolation at scale (ABAC/RBAC) and produce builder-ready technical standards such as Architecture Decision Records (ADRs).\nStrong financial acumen with the ability to track AWS spend against cost models and drive optimization through resource tagging and architectural efficiency.\nWork Environment:\nRemote work from home.\nHours of work and days are generally Monday through Friday. Specific business hours will depend on client needs.\nPhysical Demands:\nMust be able to remain in a stationary position 50% of the time.\nThe person in this position must occasionally move about inside the office to access file cabinets, library stacks, office machinery, etc.\nConstantly operates a computer and other office productivity machinery, such as a calculator, copy machine, and printer.\nThe person in this position frequently communicates with clients and coworkers. Must be able to exchange accurate information in these situations.\nData Ideology is an EEO Employer\n\n9TniuMsVnp","company":"Data Ideology","rawCompany":"data ideology","city":"Remote","state":"OR","isRemote":false,"isActive":false,"createdAt":"2026-08-04T17:04:27.181Z","occupations":[{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"15-1243.00","title":"Database Architects","slug":"database-architects"},{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"541690","title":"Other Scientific and Technical Consulting Services","slug":"other-scientific-and-technical-consulting-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Senior AI/ML Architect","description":"Data Ideology\nAt 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 www.dataideology.com\nSenior AI/ML Architect - (Contract 1099)\nWe 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\nKey Responsibilities\nLead 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.\nDesign 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.\nDesign 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.\nDesign 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.\nEvaluate 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.\nDefine 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.\nCollaborate 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.\nCollaborate 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.\nDocument 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.\nProduce 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.\nSupervisory Responsibilities: None\nQualifications\nEducation and Experience:\nBachelor’s degree in Computer Science, Engineering, or equivalent professional experience; AWS certifications (Solutions Architect Pro or Security Specialty) are highly preferred.\n7+ 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.\nExpert-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.\nDeep experience building and maintaining CI/CD pipelines for infrastructure, including environment promotion (Dev/Stage/Prod), drift detection, and automated validation.\nSolid networking fundamentals, including VPC design, PrivateLink, and identity federation patterns (SAML/OAuth2/mTLS).\nDemonstrated ability to design airtight data isolation at scale (ABAC/RBAC) and produce builder-ready technical standards such as Architecture Decision Records (ADRs).\nStrong financial acumen with the ability to track AWS spend against cost models and drive optimization through resource tagging and architectural efficiency.\nWork Environment:\nRemote work from home.\nHours of work and days are generally Monday through Friday. Specific business hours will depend on client needs.\nPhysical Demands:\nMust be able to remain in a stationary position 50% of the time.\nThe person in this position must occasionally move about inside the office to access file cabinets, library stacks, office machinery, etc.\nConstantly operates a computer and other office productivity machinery, such as a calculator, copy machine, and printer.\nThe person in this position frequently communicates with clients and coworkers. Must be able to exchange accurate information in these situations.\nData Ideology is an EEO Employer\n\n9TniuMsVnp","datePosted":"2026-08-04T17:04:27.181Z","dateModified":"2026-08-04T17:04:27.181Z","hiringOrganization":{"@type":"Organization","name":"Data Ideology","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Remote","addressRegion":"OR","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"00b7f76c5bbda43df2bc6bc2"},"url":"https://jobsearcher.com/jobs/00b7f76c5bbda43df2bc6bc2"}}