Movadex

About the Role

We are looking for a Senior Forward-Deployed Engineer/Solutions Architect  to own customer onboarding and site performance optimization for a cutting-edge edge-performance platform.

In this role, you will investigate e-commerce storefronts, design and implement caching strategies, lead customer launches, validate performance improvements, and serve as a trusted technical advisor throughout the customer journey.

This is a highly customer-facing position requiring a combination of deep technical expertise, analytical thinking, and strong communication skills.

Responsibilities

• Investigate customer e-commerce storefronts and identify performance bottlenecks.
• Design caching architectures that improve performance without compromising data integrity or personalization.
• Configure, test, and validate CDN and edge caching solutions.
• Lead customer launches and migrations from planning through production deployment.
• Diagnose and resolve caching, performance, and infrastructure-related incidents.
• Analyze Core Web Vitals, RUM metrics, and other performance indicators.
• Conduct technical customer meetings, including difficult conversations and expectation management.
• Collaborate with support engineers and contribute to their development.
• Validate and demonstrate measurable site speed improvements after launch.
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Requirements

Must Have

• 5+ years of experience in web infrastructure, CDN, performance engineering, solutions engineering, or a similar field.
• Strong understanding of HTTP caching concepts:
  • Cache keys
  • Cache invalidation and purging
  • Cookies and personalization
  • Reverse proxies
  • CDN behavior and troubleshooting
• Experience with platforms such as Cloudflare, Fastly, Akamai, Varnish, or similar technologies.
• Ability to read and analyze frontend code (HTML, JavaScript, DOM).
• Experience diagnosing performance issues in production environments.
• Strong debugging and incident investigation skills.
• Experience owning customer-facing launches or migrations end-to-end.
• Ability to communicate complex technical topics to customers clearly and confidently.

• Advanced English (spoken and written).

   

Nice to Have

• Shopify experience.
• Salesforce Commerce Cloud experience.
• Experience working in e-commerce agencies.
• Background in Solutions Engineering, Solutions Architecture, Forward-Deployed Engineering, or Technical Consulting.
• Experience with Core Web Vitals and Real User Monitoring tools.

Interview Process

• Recruiter screening
• Technical assessment
• Live working session with the engineering team

The live session includes:

• Technical discussions
• Problem-solving exercises
• Whiteboard-style reasoning and calculations
• Investigation of unfamiliar code and performance scenarios

Working Requirements

• Minimum 4 hours overlap with US Eastern Time business hours.
• Comfortable working on camera and sharing screen during customer-facing meetings.
• Ability to become independently productive after onboarding and training.

Movadex Labs is looking for a highly technical Solutions Architect to help design and deploy custom AI systems for enterprise clients. This is not a generic consulting role. You will work at the intersection of business workflows, AI infrastructure, data architecture, agentic systems, and production engineering.

You will work closely with our Business Analyst, product/enterprise design lead, and engineering team to translate messy enterprise needs into clear technical architectures, sandbox environments, implementation plans, and deployable AI solutions.

The ideal person understands how to move from “the client has a workflow problem” to “here is the architecture, data flow, model/tooling layer, security approach, integration plan, and technical execution roadmap.”

What you’ll do

Work with the BA to understand client workflows, internal systems, data sources, approval flows, users, edge cases, and business constraints.

Design technical discovery frameworks and AI sandboxes for enterprise clients, including data ingestion, knowledge layers, vector search/RAG, workflow orchestration, evaluation loops, permissions, observability, and deployment options.

Architect custom agentic systems that can operate across internal documents, structured data, APIs, tools, and human-in-the-loop processes.

Define the right AI infrastructure for each use case: LLM providers, model routing, embeddings, vector databases, orchestration frameworks, retrieval pipelines, eval systems, monitoring, guardrails, and hosting environment.

Create implementation blueprints for engineering teams, including system diagrams, integration maps, API requirements, data requirements, deployment approach, security considerations, and phased delivery plans.

Support enterprise pilots from technical discovery through sandbox build, prototype, production deployment, and post-launch optimization.

Help evaluate whether a workflow should be solved with RAG, fine-tuning, workflow automation, agent orchestration, rules-based logic, traditional software, or a combination of approaches.

Partner with product/design to make sure the AI system is not just technically functional, but actually usable, adoptable, and measurable inside the client’s organization.

Collaborate with engineers during implementation, review architecture decisions, unblock technical issues, and ensure the final system matches the intended design.

Help build reusable internal assets, including reference architectures, agent templates, sandbox environments, evaluation patterns, deployment playbooks, and technical documentation.

What we’re looking for

Strong experience designing production software systems, ideally in enterprise, AI, data, SaaS, automation, or cloud environments.

Deep understanding of modern AI infrastructure: LLM APIs, RAG, embeddings, vector databases, agent frameworks, tool calling, evals, prompt orchestration, model routing, guardrails, observability, and deployment tradeoffs.

Ability to work with messy, ambiguous enterprise requirements and turn them into clear technical plans.

Strong understanding of backend systems, APIs, databases, cloud architecture, authentication, permissions, security, and integration patterns.

Comfortable designing sandbox environments and proof-of-concept systems that can later scale into production.

Ability to communicate clearly with both technical and non-technical stakeholders.

Strong documentation skills: architecture diagrams, technical specs, implementation plans, and engineering handoff materials.

Practical, product-minded thinking. You should know when to use advanced AI infrastructure and when a simpler workflow automation or software solution is the better answer.

Experience with AWS, Azure, or GCP is strongly preferred.

Experience with Python, TypeScript/Node.js, FastAPI, LangChain/LangGraph, LlamaIndex, OpenAI/Anthropic APIs, vector DBs, or similar tools is a strong plus.

You are a strong fit if

You can sit in a client discovery session, understand the business process, ask sharp technical questions, and quickly see what kind of AI system should be built.

You are not just an engineer and not just a consultant. You can bridge strategy, architecture, and implementation.

You understand that enterprise AI is not only about prompts or models. It is about workflows, data readiness, integrations, permissions, evals, adoption, and measurable ROI.

You can design something that engineers can actually build.

Movadex Labs is looking for a highly technical Solutions Architect to help design and deploy custom AI systems for enterprise clients. This is not a generic consulting role. You will work at the intersection of business workflows, AI infrastructure, data architecture, agentic systems, and production engineering.

You will work closely with our Business Analyst, product/enterprise design lead, and engineering team to translate messy enterprise needs into clear technical architectures, sandbox environments, implementation plans, and deployable AI solutions.

The ideal person understands how to move from “the client has a workflow problem” to “here is the architecture, data flow, model/tooling layer, security approach, integration plan, and technical execution roadmap.”

What you’ll do

Work with the BA to understand client workflows, internal systems, data sources, approval flows, users, edge cases, and business constraints.

Design technical discovery frameworks and AI sandboxes for enterprise clients, including data ingestion, knowledge layers, vector search/RAG, workflow orchestration, evaluation loops, permissions, observability, and deployment options.

Architect custom agentic systems that can operate across internal documents, structured data, APIs, tools, and human-in-the-loop processes.

Define the right AI infrastructure for each use case: LLM providers, model routing, embeddings, vector databases, orchestration frameworks, retrieval pipelines, eval systems, monitoring, guardrails, and hosting environment.

Create implementation blueprints for engineering teams, including system diagrams, integration maps, API requirements, data requirements, deployment approach, security considerations, and phased delivery plans.

Support enterprise pilots from technical discovery through sandbox build, prototype, production deployment, and post-launch optimization.

Help evaluate whether a workflow should be solved with RAG, fine-tuning, workflow automation, agent orchestration, rules-based logic, traditional software, or a combination of approaches.

Partner with product/design to make sure the AI system is not just technically functional, but actually usable, adoptable, and measurable inside the client’s organization.

Collaborate with engineers during implementation, review architecture decisions, unblock technical issues, and ensure the final system matches the intended design.

Help build reusable internal assets, including reference architectures, agent templates, sandbox environments, evaluation patterns, deployment playbooks, and technical documentation.

What we’re looking for

Strong experience designing production software systems, ideally in enterprise, AI, data, SaaS, automation, or cloud environments.

Deep understanding of modern AI infrastructure: LLM APIs, RAG, embeddings, vector databases, agent frameworks, tool calling, evals, prompt orchestration, model routing, guardrails, observability, and deployment tradeoffs.

Ability to work with messy, ambiguous enterprise requirements and turn them into clear technical plans.

Strong understanding of backend systems, APIs, databases, cloud architecture, authentication, permissions, security, and integration patterns.

Comfortable designing sandbox environments and proof-of-concept systems that can later scale into production.

Ability to communicate clearly with both technical and non-technical stakeholders.

Strong documentation skills: architecture diagrams, technical specs, implementation plans, and engineering handoff materials.

Practical, product-minded thinking. You should know when to use advanced AI infrastructure and when a simpler workflow automation or software solution is the better answer.

Experience with AWS, Azure, or GCP is strongly preferred.

Experience with Python, TypeScript/Node.js, FastAPI, LangChain/LangGraph, LlamaIndex, OpenAI/Anthropic APIs, vector DBs, or similar tools is a strong plus.

You are a strong fit if

You can sit in a client discovery session, understand the business process, ask sharp technical questions, and quickly see what kind of AI system should be built.

You are not just an engineer and not just a consultant. You can bridge strategy, architecture, and implementation.

You understand that enterprise AI is not only about prompts or models. It is about workflows, data readiness, integrations, permissions, evals, adoption, and measurable ROI.

You can design something that engineers can actually build.