About the E2M:
E2M Solutions works as a trusted white-label partner for digital agencies. We support agencies with consistent and reliable delivery through services such as website design, web development, eCommerce, SEO, AI SEO, PPC, AI automation, and content writing. Founded on strong business ethics, we are an equal opportunity organization powered by 300+ experienced professionals, partnering with 400+ digital agencies across the US, UK, Canada, Europe, and Australia. At E2M, we value ownership, consistency, and people who are committed to doing meaningful work and growing together. If you’re someone who dreams big and has the gumption to make them come true, E2M has a place for you. About the Role You will own AI engagements for our agency clients, from the first discovery call to live, adopted solutions. Our clients know AI matters but are held back by manual work, inconsistent AI output, and disconnected tools.
- You will find where time and money are being lost, define what to build and in what order, make sound architecture and security calls, and lead an engineering pod to ship production-ready systems. You are the person clients trust when things go right, and just as importantly, when they go wrong.
Key Responsibilities 1. Discovery and Assessment Run a structured 3 to 4 week AI assessment with stakeholders across PPC, SEO, content, project management, sales, and leadership.
- Use pre-call audit sheets to quantify manual work (task, frequency, hours) and steer toward the highest-value workflows.
- Map workflows end to end (tools, handoffs, data sources, failure points) and reframe solution-first requests into the real underlying problem.
2. AI Blueprint and Proposal Author the client AI Blueprint: 10 to 30+ use cases, each with current process, proposed solution and stack, dependencies, expected outcome, and timeline.
- Prioritise with an Impact vs. Effort matrix and recommend 2 to 3 high-impact starting automations.
- Design phased roadmaps (quick wins, leadership visibility, revenue and lead generation, then agentic systems) that build on existing tools.
- Present the plan to founders, covering team model, delivery cadence, testing approach (for example shadow mode), and pricing.
3. Solution Architecture Lead platform, hosting, and build vs. buy decisions with client technical teams (Azure AI Foundry, Claude, OpenAI, Gemini, multi-model).
- Define secure access: least privilege, read-only API scopes, SSO/SAML, RBAC, audit trails, and exclusion of sensitive data.
- Design knowledge systems: RAG pipelines, vector search trade-offs, tiered context management, MCP connectors, and reusable Claude Skills.
- Test feasibility on client APIs before scoping, manage cost-to-serve, and make deliberate "prototype now, re-architect later" calls.
4. Delivery and Account Management Run weekly syncs per account: demo progress, collect feedback, manage change requests, and reprioritise.
- Turn feedback into clear, testable requirements for the pod and hold the line on MVP dates.
- Own AI output quality: lead root cause analysis on failures, put safe interim processes in place, and track accuracy over time.
- Lead client communication on security incidents, covering impact, actions, and timelines.
5. Adoption and Training Train client teams through live sessions, SOPs, and follow-up coaching so solutions are used after launch.
- Ensure AI output matches each client’s brand voice and coach clients on giving feedback that improves it.
6. Team Leadership Direct the delivery pod: set requirements, review builds, and enforce a production-ready definition of done, edge cases included.
- Mentor junior team members toward client-facing roles through frameworks, scenario reviews, and call shadowing.
Requirements: 1. Experience 2+ years building custom AI solutions (LLM apps, RAG, agents, workflow automation) that reached production.
- 4+ years in client-facing delivery (Project or Program Manager, Solutions Consultant, CSM, or Account Manager), ideally in an agency or services company.
- Track record of owning multiple concurrent client engagements end to end.
2. Technical Skills AI: Claude, OpenAI, Gemini, prompt and context engineering, RAG, embeddings, vector search, output evaluation.
- Build: Python with FastAPI or LangGraph, MCP servers and connectors.
- Integration: APIs, webhooks, OAuth scopes, SSO/SAML, RBAC, secure credential handling, plus one cloud (Azure, AWS, or GCP) and Supabase or Postgres.
- Agency stack: GA4, Search Console, Google, Meta and Bing Ads, Klaviyo, SEO data providers, HubSpot, and PM tools (Teamwork, Basecamp, ClickUp, Monday, Productive).
- Judgment: Can sketch an architecture, estimate effort, and spot feasibility risks without an engineer in the room.
3. Consulting Skills Facilitates discovery workshops that surface the real problem.
- Quantifies ROI (hours saved, turnaround time, cost avoided) and uses it to prioritise.
- Writes clear proposals, Blueprints, requirements, and SOPs.
- Presents confidently to executives and discusses pricing, phasing, and scope in plain business language.
Nice to Have Experience productising a custom AI solution across multiple clients.
- PMP, PRINCE2, or equivalent delivery certification.
- Consulting background with exposure to process improvement and quality frameworks.
- Experience with AI search visibility, SEO intelligence, or marketing analytics.
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