**Job description**
**About this role**:
We are building **Altitude Intelligence** — the AI engine that turns Reunion Marketing's strategy, live client performance data, and market data into one queryable system of record for our automotive dealer clients. Over the next 12 months it powers three named outputs:
- **AI Client Summaries** — client-ready performance narratives drafted from live data across KeyLift (SEO), Endeavor (LLM/answer-engine visibility), LocalEyes (Google Business Profile), Adapt (Paid Advertising), and Market Data.
- **Proactive Performance Visibility** — anomalies and at-risk accounts routed to the SEO, Paid, GEO, GA4, and Client Success teams _before_ the client sees them.
- **Market Intelligence -**dealer-level reports that frame each market (Backyard / Battleground / Rival), quantify share, inventory, and visibility gaps, and land on a single strategic recommendations.
This is a senior, hands-on role. It is meant for someone who has built and operated production LLM systems before and is comfortable owning the quality bar end-to-end.
**- Why this is a rare opportunity**:
Reunion has the kind of AI surface area most engineers want but rarely get:
- **Real data.**Five product datasets, 15+ third-party APIs, Redshift, HubSpot, unstructured strategy documents, market data, and client history.
- **Real consequences.** If the system is wrong, the failure does not stay inside a demo. It can show up in a client conversation.
- **A clear 12-month target.**The business outcomes are defined. The product direction is clear. The hard work is the engineering architecture, evaluation, reliability, and quality system that makes it production-grade.
- **Open architecture with serious constraints.**The high-level shape exists: Knowledge + Context MCP reasoning engine review distribution. The direction is clear,



but many of the important implementation decisions are still open, and this hire will help make them.
**What you'll own**:
You will design and own the full LLM pipeline from raw data and strategy documents to client-ready summaries, alerts, and recommendations.
This includes:
- Versioned input and output schemas per product
- Prompt management as governed, reviewable artifacts
- Dynamic payload generation from live APIs, warehouse tables, and client context
- Structured output generation
- Product-specific context assembly
- Client-specific history and strategy context
- Output templates for different teams and audiences
**2. Retrieval and knowledge architecture**:
You will design the retrieval system that grounds the model in Reunion’s strategy library, product data, client history, and market context.
This is a hybrid retrieval problem. The answer will not be “just vector search.”
You will decide when to use:
- SQL
- Vector search
- Lexical search
- Reranking
- Graph retrieval
- Tool calls
- Composed retrieval pipelines
You will also help define the knowledge graph layer for relationship-heavy problems such as:
- Dealer market competitor
- Inventory campaign keyword
- Citation answer-engine visibility
- Client product performance trend
- Market ZIP segment opportunity
The goal is not to use a graph because it sounds interesting.



The goal is to know exactly when graph-augmented retrieval is the right tool and when it is not.
**3. Agentic workflows and orchestration**:
You will design and ship the multi-step reasoning workflows behind:
- Monthly and quarterly AI Client Summaries
- Opportunity identification
- Market re-evaluation
- Inventory and sales alignment
- At-risk account flagging
- Anomaly explanation and routing
These workflows need to be durable, observable, retryable, replayable, and cancellable. They cannot be fragile request handlers with hidden state and unclear failure modes.
The orchestration substrate is still open. You will have a strong voice in that decision.
**4. MCP tool surfaces**:
You will stand up and operate the MCP servers that expose Reunion’s knowledge and client context to the reasoning engine.
This includes:
- Knowledge MCP: methodology, frameworks, strategy library, product context
- Context MCP: client data from HubSpot, Altitude, Redshift, and connected product systems
You will own the production characteristics:
- Typed inputs and outputs
- Auth and tenant scoping
- Rate limiting
- Structured responses
- Observability
- Graceful degradation
- Clear failure handling
**5. Evaluation, quality, and feedback loops**:
You will build the evaluation system end-to-end.
That includes:
- Offline experiments
- Regression suites in CI
- Online scoring of production traces
- Factuality checks
- Structured output validation
- Voice and tone fidelity
- Cost and latency tracking
- Safety and client-readiness gates
You will also close the loop between human feedback and system improvement.
Reviewer edits, Client Success feedback, Gong tags, and Pendo engagement should become structured signal that improves prompts, retrieval, datasets, and evaluati

📌 Senior Ai/llm Engineer - Remote Latam (Monterrey)
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📍 Monterrey