Hire an AI Application Engineer: Production LLM Agents, Hybrid Graph + Vector RAG, MCP Servers & Google ADK

Moving an AI feature from a fragile prompt prototype to a deterministic, low-latency production system requires an experienced AI Application Engineer. I am Aldian Fazrihady, a Senior AI Application Engineer and Full Stack Architect who has shipped production AI platforms including Segnog (autonomous AI workspace), IchibanAI (multimodal video/audio AI pipeline), and custom enterprise RAG & agentic systems.

1. Production AI Application Engineering Capabilities

• Autonomous Multi-Agent Systems (Google ADK & LangGraph): Deterministic state machines, tool-calling guardrails, human-in-the-loop approval gates, and persistent session checkpoints.
• Model Context Protocol (MCP) Servers: Custom MCP servers and lazy-loaded tool schemas that connect Claude, Gemini, and OpenAI agents safely to internal PostgreSQL databases, FHIR healthcare APIs, CRMs, and cloud infrastructure.
• Hybrid Graph + Vector RAG (pgvector & FalkorDB): Combining dense semantic vector retrieval with knowledge-graph traversal and reciprocal rank fusion (RRF) to eliminate hallucinations on complex domain data.
• Low-Latency Structured Outputs (Gemini 2.5 Flash/Pro, Claude Sonnet, OpenAI): Enforcing strict JSON Schema / Pydantic responses with zero thinking-budget overhead for sub-second interactive UIs.

2. Sub-Second Structured JSON Feasibility & Scope Classifier Pattern

import requests

def evaluate_requirement_structured(prompt: str, api_key: str) -> list[str]:
    resp = requests.post(
        f"https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent?key={api_key}",
        json={
            "contents": [{"parts": [{"text": prompt}]}],
            "generationConfig": {
                "temperature": 0.2,
                "responseMimeType": "application/json",
                "thinkingConfig": {"thinkingBudget": 0},
            },
        },
        timeout=4.0,
    )
    resp.raise_for_status()
    return resp.json()

3. What an AI Application Engineer Can Deliver in 1 Day ($100 / Rp 1,000,000)

• Build and deploy a custom Model Context Protocol (MCP) server or Google ADK agent tool connected to your API or database.
• Implement a hybrid RAG ingestion and retrieval pipeline using PostgreSQL pgvector with citation tracking.
• Convert an unreliable free-text LLM prompt into a schema-validated JSON extraction or classification endpoint with automated fallback handling.