About WebLife
For over 16 years, WebLife has been a leader in the e-commerce industry, operating category-defining stores and becoming one of the largest distributors of mailboxes in the US. Our success comes from building data-driven operations and high-performing teams that thrive in fast-moving markets.
Now, we are taking that foundation into a bold new venture—building an AI-powered product that will redefine how small and mid-sized businesses operate. Friday Solutions is a new team spun out from WebLife Labs—an early-stage, product-first SaaS venture developing role-aware assistants and knowledge workflows. Backed by the stability and resources of WebLife’s 16-year track record, Friday Solutions moves fast, values technical ownership, and expects every hire to influence product design, architecture, and operational excellence.
By combining the agility of a startup with the backing of a proven e-commerce leader, we are creating an environment where ideas turn into impact—and where you can help write the next chapter of business with AI.
About the Role
We’re hiring a Solutions Architect to own system design, architecture, and technical execution for Friday Solutions. This is a hands-on leadership role where you’ll both design the system and ship code with a small, high-impact team.
You’ll own the end-to-end technical architecture for our multi-tenant SaaS platform (knowledge ingestion, embeddings/vector store, retrieval, model integration, API surface), translate product requirements into robust, secure, and scalable solutions, and act as the technical authority for cross-functional teams.
This is a remote role and a unique opportunity to shape a global product at its earliest stage while working hands-on as part of a small, high-performing team.
Responsibilities
- Design and document production architecture for multi-tenant knowledge and retrieval services (tenant isolation, storage strategy, index topology, latency, and cost trade-offs).
- Build and own CI/CD for application and model artifacts, including automated tests, model registry, rollout strategies (canary, shadow, A/B), and rollback processes.
- Integrate model inference pipelines (real-time and batch) and ensure performant, cost-controlled serving.
- Define data ingestion, schema evolution, and governance policies; collaborate with the Data Architect/Engineer to operationalize pipelines and lineage.
- Implement observability and SLO/SLI practices for model and app performance (monitoring, alerting, incident runbooks).
- Mentor engineers, lead code reviews, and shape hiring and onboarding for the engineering team.
- Produce actionable technical documentation, including architecture diagrams, API contracts, runbooks, and capacity plans.
- Evaluate and select third-party services (vector databases, feature stores, orchestration tools) and manage vendor integrations.
- Participate in product scoping and prioritize technical debt versus feature delivery.
- Lead and mentor engineers, run the engineering delivery cadence, and remain hands-on in code and infrastructure.
- Act as the technical authority for cross-functional teams (Product, Data, Design, Ops) and lead technical conversations with partners or vendors as needed.
Requirements
Must-Have Qualifications
- 6+ years of engineering experience, including 2+ years in a technical lead or architect role.
- Proven experience designing and operating SaaS systems and multi-tenant architectures.
- Hands-on experience with production ML/AI systems (model serving, inference infrastructure, or retrieval-augmented systems).
- Strong backend engineering skills (Python, Go, or Node) and cloud-native deployments (AWS, GCP, or Azure).
- Experience with container orchestration (Kubernetes) and infrastructure as code (Terraform or CloudFormation).
- Track record of building CI/CD pipelines and production monitoring (SLOs, alerting, observability).
- Excellent communication skills with the ability to translate product requirements into technical scope and mentor engineers.
Preferred Qualifications
- Direct MLOps or platform engineering experience (training pipelines, registries, experiment tracking).
- Experience with vector stores or search infrastructure (Pinecone, Qdrant, Milvus, Elasticsearch/OpenSearch).
- Prior early-stage startup experience or product launches.
- Familiarity with feature stores, data governance, privacy controls, and compliance.
- Experience with performance optimization and cost engineering for inference workloads.
What We Offer
- Competitive USD-based compensation packages.
- AI-driven, innovation-focused projects.
- A culture that encourages curiosity and lifelong learning.
- Clear career paths and personal development opportunities.
- A flexible, work-from-home setup as part of a globally connected team.
The stability of an established 16-year e-commerce company combined with the agility of an early-stage product team.
Join us to build a global product from the ground up and redefine how businesses run with AI.
