Solution Consultant, Consumer Brands

Accelerize360 (A360) is an AI-enabled consulting and technology firm. We design, build, and run applications, data, integration, and AI solutions for companies that sell to consumers.

The Consumer Brands practice serves mid-market to enterprise retail, CPG, apparel, beauty, footwear, and direct-to-consumer businesses, typically in the $500M to $5B revenue range. These companies carry enterprise-grade complexity (multi-channel commerce, distributed fulfillment, high-volume customer care, wholesale and DTC operating side by side) without the budget, timeline, or internal bench that a multi-year Big 4 transformation assumes. That gap is our market, and specialization is how we win it.

A360 is also an Anthropic partner. Generative AI is not an adjacent offering here. It is embedded in how we discover, design, build, test, and support, and it is a condition of employment on this team rather than a differentiator a few people carry.

A360 delivery is reorganizing around Forward Deployed Engineering: one integrated team with continuous accountability, rather than a consulting group that gathers requirements and a technology group that builds against them. There is no simple handoff between the person who shapes the solution and the person who owns whether it works in production.

The Solution Consultant is an independent workstream owner. You carry a defined scope from discovery and design through build, integration, testing, deployment readiness, handoff, and hypercare. You are the client-facing owner of that scope: you run the sessions, make defensible design decisions inside engagement guardrails, review the output of associate engineers before it reaches the client, and manage dependencies across the rest of the team.

A commercially capable Solution Consultant II may lead a clearly bounded single-workstream implementation or a defined managed services scope end to end, with delivery governance in place and an L3 as escalation backstop.

• Ownership. A defined workstream is yours from requirements through hypercare, including quality, deployment readiness, and what happens after go-live.
• Domain. Deep application in consumer brands: customer care and contact center, commerce, order management and fulfillment, store and POS, customer data.
• AI-native delivery. GenAI applied across the full lifecycle, not just code generation, with the evaluation discipline to know whether the output is actually good.
• Platform agnostic. You pick up new platforms without friction and choose the right mix of application, data, integration, automation, and AI. Salesforce depth is welcome, not required.

• Own a defined workstream from requirements and success criteria through solution design, build and configuration, integration, testing, deployment readiness, documentation, and handoff.
• Build, demo and follow-up on rapid POCs using tools like ClaudeCode to support pre-sales and deep discovery. All with the goal of generating interest in new projects and opportunities
• Create and maintain an executable workstream plan covering estimates, milestones, dependencies, acceptance criteria, risks, and decisions.
• Translate business and technical requirements into a working solution independently, escalating only when a decision exceeds workstream authority or creates material engagement risk.
• Manage requirement changes, scope pressure, and competing priorities while protecting quality and the critical path.
• Lead bounded proofs of concept and prototypes, validate results objectively against agreed exit criteria, and document the cost, risk, and scope implications of moving to build.
• Own workstream deployment readiness: runbook, cutover steps, rollback, observability, support transition, documentation, and hypercare closure criteria.

• Run discovery, design, working, and demonstration sessions for the assigned workstream.
• Serve as the primary client contact for workstream status, decisions, trade-offs, dependencies, and risks.
• Explain technical options in business terms and connect solution choices to client outcomes, cost, timeline, security, and maintainability.
• Navigate routine difficult conversations (missed expectations, requirement ambiguity, scope pressure) with preparation, facts, and appropriate escalation.
• Partner with Change Management (Program/Project Manager) on stakeholder readiness, adoption, training, and benefit evidence, and with Advisory when strategic outcomes or executive expectations shift.

• Make defensible workstream architecture and design decisions within engagement standards and explain the trade-offs to technical and non-technical audiences.
• Review associate engineer output before client exposure and confirm that requirements, test evidence, documentation, security, and deployment standards are met.
• Own the workstream test strategy and evidence across functional, regression, integration, data, performance, security, and AI evaluation dimensions as applicable.
• Design for operational reality: data handling, error paths, monitoring, support ownership, user adoption, and maintainability after the team rolls off.
• Diagnose delivery and technical issues, coordinate resolution across teams, and document reusable lessons.

This section is a requirement of the role, not an enhancement to it.

• Use approved AI capabilities to compress discovery synthesis, solution design, engineering, testing, documentation, and analysis across the full delivery lifecycle.
• Design and build GenAI into client solutions where it creates measurable value: retrieval and knowledge grounding, summarization, classification and routing, agentic workflows, and conversational interfaces.
• Select and recommend AI-assisted workflows appropriate to the workstream, weighing quality, security, privacy, cost, latency, reliability, and client policy.
• Establish review and validation practices for AI-generated output, define evaluation criteria before build, and coach associate engineers in responsible use.
• Know the difference between a demo and a production system, and be able to state exactly what has to change to close that gap.
• Follow A360 and client data-use rules without exception. Never enter client data into unapproved tools and never ship unvalidated AI output.

• Apply working knowledge of consumer brand operating models: customer care and contact center operations, B2C and B2B commerce, order management and fulfillment, store and point of sale, loyalty and customer data.
• Understand the economics that drive client decisions in this vertical, including peak season and seasonality, unit economics, customer lifetime value, and the cost of service versus its revenue impact.
• Translate retail, CPG, and brand-side business language into system requirements without requiring the client to speak in technical terms.
• Bring pattern recognition from prior consumer engagements into design decisions rather than starting from a blank page each time.

• Break work into clear assignments for associate engineers, provide context and acceptance criteria, and give direct, actionable feedback.
• Coach developing team members through pairing, reviews, demonstrations, and retrospective learning.
• Hold the workstream to quality standards when an L3 is not in the room.
• Contribute reusable templates, reference architectures, accelerators, prompts, evaluations, and implementation patterns to A360 knowledge repositories.

• Understand how workstream staffing, sequencing, rework, and timeline decisions affect margin and client value.
• Contribute estimates, assumptions, dependencies, risks, and technical content to statements of work, change requests, and project plans.
• Learn new concepts, domain knowledge, technologies very very quickly 
• Identify legitimate client growth opportunities or adjacent needs and route them to the appropriate Client Development, Advisory, Sales, or Delivery leader with a clear business case.
• Think past the current deliverable to adoption, expansion, future phases, and reusable value.

• 6+ years in delivery-facing consulting, engineering, or product roles, including 3+ years client-facing in a consulting or professional services environment.
• Demonstrated success independently owning a bounded technical workstream on a live client engagement, from requirements through deployment.
• Strong hands-on capability in at least one technical domain and working breadth across at least two, drawn from application and platform configuration, custom development, cloud, data engineering, integration and APIs, automation, or quality engineering.
• Demonstrated GenAI capability as described in the section above, in both how you work and what you build.
• Technology-agnostic by disposition. You pick up new platforms, frameworks, and tools quickly and you are not defined by a single stack.
• Ability to facilitate discovery, translate requirements, design solutions, estimate work, and manage delivery through go-live and handoff.
• Experience reviewing other people’s work and providing direct, constructive coaching and quality guidance.
• Client-facing communication skills strong enough to explain a technical trade-off to a VP of Customer Care and a staff engineer in the same meeting.
• Working understanding of professional services economics: scope control, change control, utilization, and margin.
• Sound judgment on security, data handling, responsible AI, and production readiness.

• Consumer vertical experience. Retail, CPG, apparel, beauty, footwear, specialty, or direct-to-consumer, delivered client-side or as a consultant.
• Salesforce ecosystem familiarity. Service Cloud, Sales Cloud, Commerce, Order Management, Data Cloud, or Agentforce. Valued and useful here, not a requirement.
• Contact center depth. CCaaS platforms, telephony migration to cloud-native, omnichannel routing, knowledge management, or AI agent deployment in a service context.
• Commerce and order management. B2B or B2C commerce, OMS, or fulfillment workstreams, including ERP integration (SAP, AS400, NetSuite, or similar).
• Agentic AI exposure. Agent design, orchestration frameworks, tool use, and evaluation or testing of AI systems in production contexts.
• Data platform experience. Snowflake, Databricks, dbt, or comparable, including pipeline work you led or built alongside rather than only consumed.
• Presales contribution. You have joined client conversations with a commercial purpose and shaped an estimate or SOW input.
• Practice building. You have contributed an accelerator, offering, standard, or capability beyond your immediate engagement.