Senior AI/ML Engineer - WFH/Remote

Work Set Up: Fully remote in the PH on a nightshift schedule (8PM-5AM)

This role requires a practitioner who can architect, deploy, and operate production-grade AI agents within the Google Cloud ecosystem, with a deep specialization in extracting structured data from messy, unstructured documents.

1. Advanced Document Intelligence & Data Extraction

Multimodal & VLM Extraction

Utilizing Vision-Language Models (like Gemini) for direct spatial reasoning, visual QA, and extracting data from complex layouts (images, scanned PDFs) without relying solely on intermediary OCR.

Structured Output Enforcement

Forcing LLMs to return deterministic, strictly typed data using JSON schemas, Pydantic, and Function Calling/Tool Use.

Layout & Spatial Analysis

Understanding and parsing complex document structures, including nested tables, multi-column layouts, and form fields using bounding box coordinates and layout-aware parsers.

OCR & Traditional NLP

Deep understanding of when to bypass LLMs in favor of faster, cheaper techniques.

Experience with:

• OCR Engines: Google Cloud Vision API, Tesseract, or Document AI base parsers.
• NER (Named Entity Recognition): Training or utilizing models (e.g., spaCy) to extract specific entities (names, dates, organizations).
• Heuristics & RegEx: Building robust fallback mechanisms and regex pipelines for highly standardized, deterministic fields (e.g., routing numbers, standard invoice IDs).

Chunking & RAG for Long Documents

Designing intelligent document chunking strategies (semantic, page-based, or structural) to feed relevant context into LLMs without exceeding context windows or degrading recall.

2. Production Readiness, Observability & MLOps

Instrumentation & Tracing

Instrumenting AI agents and LLM chains to track the entire execution path.

Experience with LLM observability tools (e.g., LangSmith, Arize Phoenix, or OpenTelemetry) to debug complex agent routing decisions.

Metering & Cost Tracking

Implementing strict token metering to track API usage and calculate the cost-per-document.

Ability to optimize prompts and model selection (e.g., routing simpler tasks to Gemini Flash) to control production costs.

Monitoring & Alerting

Setting up robust dashboards and alerts (using Google Cloud Monitoring/Operations Suite) to track:

• System latency and LLM response times
• API rate limits, quotas, and timeout errors
• Schema validation failure rates (e.g., when the LLM hallucinates an incorrect JSON structure)

Structured Logging & Auditability

Designing detailed, structured logging for every document processed to create a clear audit trail (crucial for financial/compliance use cases), tracking the source file, extracted data, and confidence scores.

CI/CD for AI

Experience managing prompt versions, testing agent logic against Golden Datasets (evals) in CI pipelines, and safely deploying updates without breaking downstream dependencies.

3. Core AI & Agentic Systems

Agentic Architectures

Designing multi-agent systems where an Orchestrator agent routes tasks to specialized sub-agents (e.g., separating Invoice processing from Contract analysis) using frameworks like LangGraph, LangChain, or Google's Agent Development Kit (ADK).

Prompt Engineering

Advanced chain-of-thought, few-shot prompting, and dynamic prompt injection.

Hallucination Control

Implementing validation layers, self-reflection loops, and grounding techniques to ensure the accuracy of extracted financial data.

4. Google Cloud Platform (GCP) & Backend Integration

Vertex AI Suite

Hands-on mastery of the Vertex AI ecosystem, including Model Garden, Vertex AI Studio, and custom model deployment.

Compute & Event-Driven Architecture

Deploying AI microservices via Cloud Run or Cloud Functions, triggered by events in Cloud Storage (GCS) or Pub/Sub message queues.

Python Engineering

Expert-level Python for data manipulation (Pandas, NumPy) and API development (FastAPI or Flask)

Qualifications

• At least 3 years of experience in Google Cloud Platform (GCP), Python, FastAPI, Flask (Python)
• At least 2 years of experience in Google Cloud Run, Google Cloud Storage, Pub/Sub, Pandas
• At least 1 year of experience in Vertex AI, Gemini, Function Calling, LangChain