AI Application Engineer Blueprint: Building Production MCP Servers, Hybrid pgvector + Graph RAG & Deterministic Agent Evaluations

Hiring a dedicated AI Application Engineer is about moving beyond fragile prompt wrappers to build deterministic, observable, and permission-aware AI systems. Having built and deployed production AI platforms like Segnog and IchibanAI, I focus on three pillars: Model Context Protocol (MCP) tool servers, Hybrid Search RAG (Reciprocal Rank Fusion over pgvector + BM25), and automated LLM evaluation harnesses.

1. Implementing Reciprocal Rank Fusion (RRF) for Hybrid RAG in PostgreSQL

Pure vector similarity search frequently misses exact SKU codes, legal clause identifiers, or medical codes, while pure keyword search misses semantic synonyms. A production AI Application Engineer combines both in a single PostgreSQL CTE query using Reciprocal Rank Fusion (RRF):

WITH semantic_matches AS (
    SELECT
        doc_id,
        chunk_text,
        ROW_NUMBER() OVER (ORDER BY embedding <=> %(query_embedding)s::vector) AS vec_rank
    FROM knowledge_chunks
    WHERE tenant_id = %(tenant_id)s
    ORDER BY embedding <=> %(query_embedding)s::vector
    LIMIT 40
),
lexical_matches AS (
    SELECT
        doc_id,
        chunk_text,
        ROW_NUMBER() OVER (ORDER BY ts_rank_cd(search_tsv, plainto_tsquery('english', %(query_text)s)) DESC) AS lex_rank
    FROM knowledge_chunks
    WHERE tenant_id = %(tenant_id)s
      AND search_tsv @@ plainto_tsquery('english', %(query_text)s)
    LIMIT 40
)
SELECT
    COALESCE(s.doc_id, l.doc_id) AS doc_id,
    COALESCE(s.chunk_text, l.chunk_text) AS chunk_text,
    COALESCE(1.0 / (60 + s.vec_rank), 0.0) + COALESCE(1.0 / (60 + l.lex_rank), 0.0) AS rrf_score
FROM semantic_matches s
FULL OUTER JOIN lexical_matches l ON s.doc_id = l.doc_id
ORDER BY rrf_score DESC
LIMIT 10;

2. Designing Secure Model Context Protocol (MCP) Tool Servers

When exposing internal databases, ERP APIs, or cloud infrastructure tools to AI agents via Model Context Protocol (MCP), every tool handler must enforce strict JSON Schema validation, tenant isolation, and human-in-the-loop approval gates for state-mutating operations:

• Read-Only vs. Mutating Tool Separation: Tag every MCP tool with explicit idempotency and risk metadata so the orchestrator (Google ADK or LangGraph) pauses for user confirmation before executing destructive or external actions.
• Bounded Context Output: Truncate and paginate tool responses with structured summaries so a single large SQL or log query cannot overflow the agent's context window.
• Regression Evaluation Datasets: Run golden-dataset evaluations scoring tool selection accuracy, groundedness, and p95 latency on every pull request.

3. Related AI Application Engineering Guides

• Hire a Senior AI Application Engineer: LLM Agents, RAG, MCP, Google ADK & LangGraph Pillar Guide
• AI Application Engineer Category Hub