Enterprise AI Engineer (GCP)
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Job Location:
Atlanta, GA - USA
Monthly Salary:
Not Disclosed
Posted on:
2 hours ago
Vacancies:
1 Vacancy
Job Summary
Job Description: Enterprise AI Engineer (GCP)
Location: Remote / Hybrid Focus: Agentic AI Data Intelligence and Enterprise Scale
Role Overview
We are looking for a Principal Enterprise AI Engineer to architect and deliver high-impact AI
solutions within the Google Cloud ecosystem. This role is designed for a technical leader who
can bridge the gap between complex data landscapes and autonomous AI systems. You will lead
the development of Agentic AI frameworks and Data Intelligence platforms that drive
significant digital transformation for global enterprise clients.
Core Responsibilities
Architect Agentic Systems: Design and deploy multi-agent orchestration frameworks
using Vertex AI Agent Builder LangGraph or CrewAI to automate complex multi-step
business workflows.
Master RAG Architectures: Build and optimize high-performance Retrieval-
Augmented Generation (RAG) systems ensuring LLMs are grounded in enterprise data
across BigQuery and Databricks.
Model Strategy & Optimization: Select and fine-tune models within the Gemini 1.5
family balancing high-reasoning capabilities (Pro) with high-speed efficiency (Flash) for
production-grade latency.
Legacy Transformation: Lead the strategic migration of legacy analytics logic (e.g.
SAS environments) into modern AI-powered cloud architectures.
GTM Collaboration: Work closely with Go-To-Market (GTM) leadership to translate
technical AI roadmaps into measurable business value for C-suite stakeholders.
Required Skill Requirements
1. Agentic AI & Orchestration
Framework Mastery: Expert implementation of LangChain LangGraph or
LlamaIndex for stateful autonomous agent development.
Advanced Prompting: Proficiency in Chain-of-Thought (CoT) ReAct patterns and
system instruction optimization to ensure reliable model output.
Function Calling: Experience building custom tools that allow LLMs to interact
securely with enterprise APIs and SQL databases.
2. Data Intelligence & Engineering
Hybrid Data Ecosystems: Deep experience integrating Google Cloud AI services with
Databricks (Delta Lake) for unified data intelligence.
Vector Engineering: Proficiency with Vertex AI Vector Search (formerly Matching
Engine) and embedding strategies for large-scale semantic search.
Data Flow: Skill in building scalable pipelines using Dataflow or Spark to process
unstructured data for AI readiness.
3. LLMOps & Production Engineering
Evaluation Frameworks: Ability to build automated "LLM-as-a-judge" evaluation
pipelines to track accuracy faithfulness and hallucination rates.
Cloud Infrastructure: Mastery of the Vertex AI suite (Studio Model Garden Pipelines)
and Infrastructure as Code (Terraform).
Programming: Expert-level Python (FastAPI Pydantic) and advanced SQL.
4. Strategic Governance
Responsible AI: Implementation of safety filters PII redaction and ethical AI
monitoring.
Business Translation: Ability to convert technical metrics (latency token costs) into
business KPIs (ROI process efficiency).
Qualifications
Experience: 8 years in Software Engineering or Data Science with at least 3 years
focused on production-grade AI/ML.
Education: B.S./M.S. in Computer Science AI or a related quantitative field.
Certifications: Google Professional Machine Learning Engineer or Professional Cloud
Architect (preferred).
Technology Stack
AI/ML: Vertex AI Gemini 1.5 Pro/Flash PyTorch.
Data: BigQuery Databricks Vertex Vector Search.
Orchestration: LangGraph Vertex AI Agent Builder.
DevOps: GitHub Actions Terraform Vertex AI Pipelines.
Location: Remote / Hybrid Focus: Agentic AI Data Intelligence and Enterprise Scale
Role Overview
We are looking for a Principal Enterprise AI Engineer to architect and deliver high-impact AI
solutions within the Google Cloud ecosystem. This role is designed for a technical leader who
can bridge the gap between complex data landscapes and autonomous AI systems. You will lead
the development of Agentic AI frameworks and Data Intelligence platforms that drive
significant digital transformation for global enterprise clients.
Core Responsibilities
Architect Agentic Systems: Design and deploy multi-agent orchestration frameworks
using Vertex AI Agent Builder LangGraph or CrewAI to automate complex multi-step
business workflows.
Master RAG Architectures: Build and optimize high-performance Retrieval-
Augmented Generation (RAG) systems ensuring LLMs are grounded in enterprise data
across BigQuery and Databricks.
Model Strategy & Optimization: Select and fine-tune models within the Gemini 1.5
family balancing high-reasoning capabilities (Pro) with high-speed efficiency (Flash) for
production-grade latency.
Legacy Transformation: Lead the strategic migration of legacy analytics logic (e.g.
SAS environments) into modern AI-powered cloud architectures.
GTM Collaboration: Work closely with Go-To-Market (GTM) leadership to translate
technical AI roadmaps into measurable business value for C-suite stakeholders.
Required Skill Requirements
1. Agentic AI & Orchestration
Framework Mastery: Expert implementation of LangChain LangGraph or
LlamaIndex for stateful autonomous agent development.
Advanced Prompting: Proficiency in Chain-of-Thought (CoT) ReAct patterns and
system instruction optimization to ensure reliable model output.
Function Calling: Experience building custom tools that allow LLMs to interact
securely with enterprise APIs and SQL databases.
2. Data Intelligence & Engineering
Hybrid Data Ecosystems: Deep experience integrating Google Cloud AI services with
Databricks (Delta Lake) for unified data intelligence.
Vector Engineering: Proficiency with Vertex AI Vector Search (formerly Matching
Engine) and embedding strategies for large-scale semantic search.
Data Flow: Skill in building scalable pipelines using Dataflow or Spark to process
unstructured data for AI readiness.
3. LLMOps & Production Engineering
Evaluation Frameworks: Ability to build automated "LLM-as-a-judge" evaluation
pipelines to track accuracy faithfulness and hallucination rates.
Cloud Infrastructure: Mastery of the Vertex AI suite (Studio Model Garden Pipelines)
and Infrastructure as Code (Terraform).
Programming: Expert-level Python (FastAPI Pydantic) and advanced SQL.
4. Strategic Governance
Responsible AI: Implementation of safety filters PII redaction and ethical AI
monitoring.
Business Translation: Ability to convert technical metrics (latency token costs) into
business KPIs (ROI process efficiency).
Qualifications
Experience: 8 years in Software Engineering or Data Science with at least 3 years
focused on production-grade AI/ML.
Education: B.S./M.S. in Computer Science AI or a related quantitative field.
Certifications: Google Professional Machine Learning Engineer or Professional Cloud
Architect (preferred).
Technology Stack
AI/ML: Vertex AI Gemini 1.5 Pro/Flash PyTorch.
Data: BigQuery Databricks Vertex Vector Search.
Orchestration: LangGraph Vertex AI Agent Builder.
DevOps: GitHub Actions Terraform Vertex AI Pipelines.
Required Skills:
Data
