Note: The job is a remote job and is open to candidates in USA. Accelerize360 is an AI-enabled consulting and technology firm that designs, builds, and runs applications, data, integration, and AI solutions for consumer-focused companies. The Solution Consultant owns client-facing workstreams end to end, from discovery and solution design through build, testing, deployment readiness, handoff, and hypercare, while applying GenAI and consumer brands expertise. The role also includes client leadership, quality oversight, team coaching, and commercial contribution.
Responsibilities
- 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
- 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
Skills
- 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
- Working understanding of professional services economics: scope control, change control, utilization, and margin
- Sound judgment on security, data handling, responsible AI, and production readiness
Company Overview
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