Senior AI/ML Architect

This is a fully remote position, open to applicants in United States.

📋 Description

• Lead the evaluation and selection of Small Language Model (SLM) candidates: analyze options for edge deployment based on hardware limitations, inference latency requirements, domain restriction feasibility, and licensing considerations.

• Develop a technology assessment that includes a clear rationale for trade-offs and a recommended strategy.

• Design the architecture for domain restrictions and guardrails: outline how the SLM will be confined to a defined operational scope, prevent out-of-domain responses, and ensure the system adopts a retrieval-first, non-authoritative approach suitable for a safety-adjacent environment.

• Create a capability framework that details how the system will respond to operator queries — including how capabilities are scoped and isolated, how the framework accommodates the incremental addition of new interaction types over time, and what will be implemented in the prototype.

• Design the retrieval-augmented inference pipeline: specify how the SLM retrieves context from a local knowledge store during inference, covering the retrieval strategy, context injection method, and latency budget suitable for the edge environment.

• Assess potential cloud services for knowledge retrieval, model governance, and fleet-level model lifecycle management, including over-the-air model distribution to edge devices.

• Formulate architecture recommendations that align with client enterprise standards; all service selections will require client review and approval.

• Define the offboard ML lifecycle: establish how models are evaluated, adapted through prompting and retrieval enhancement, versioned, governed, and distributed at scale.

• Note that fine-tuning or custom model training is not a standard commitment during this phase — the adaptation approach will be determined based on discovery outcomes.

• Work in collaboration with the Edge ML / Embedded Engineer to consider hardware constraints that influence SLM selection and inference pipeline design, ensuring architecture recommendations are based on confirmed runtime feasibility.

• Partner with the AWS Solutions Architect to design candidate cloud service architectures for model governance, knowledge retrieval, and the model update pipeline, ensuring alignment of cloud-side AI architecture with the broader platform.

• Document safety design principles and operational boundaries — including authority separation, bounded AI behavior, explainability strategies, and human-in-the-loop considerations — as architectural artifacts for client engineering and compliance reviews.

• Present all architecture recommendations as Architecture Decision Records (ADRs) with a clear rationale for trade-offs.

• Distinguish between confirmed decisions and those pending hardware specifications or interface access that have not yet been verified.

⛳️ Requirements

• A Bachelor’s degree in Computer Science, Engineering, or equivalent professional experience.

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

• A minimum of 7 years of experience in Cloud Infrastructure or Platform Engineering, with a proven track record in leading multi-tenant AWS data platforms and event-driven architectures.

• Expert-level hands-on experience with core AWS services (S3, Glue, Redshift, Lake Formation, IoT Core, KMS) and the ability to author complex Terraform modules with remote state management.

• Extensive experience in building and maintaining CI/CD pipelines for infrastructure, including environment promotion (Dev/Stage/Prod), drift detection, and automated validation.

• Solid understanding of networking fundamentals, including VPC design, PrivateLink, and identity federation patterns (SAML/OAuth2/mTLS).

• Proven ability to design secure data isolation at scale (ABAC/RBAC) and to produce builder-ready technical standards such as Architecture Decision Records (ADRs).

• Strong financial acumen with the capability to monitor AWS spending against cost models and drive optimization through resource tagging and architectural efficiency.

🏝️ Benefits

• Work remotely from home.

• Specific business hours will be determined based on client needs.