Senior AI Engineer – Google Cloud Platform (GCP)

Virtue America; North Carolina; Hybrid; USD 150,000 - 210,000; Full time

Job summary

Virtue America is seeking an experienced Senior AI Engineer with hands-on GCP, Python, and enterprise AI architecture expertise. You will design, develop, and deploy scalable AI solutions leveraging NLP, LLMs, and RAG pipelines.

You will implement MLOps/LLMOps, build data pipelines, and collaborate with data engineers, cloud architects, and developers to deliver secure, high-performance AI applications.

Qualifications

• 12+ years in IT/software/AI/ML engineering or related field.
• Hands-on with GCP and cloud-based app development.
• Advanced Python programming; strong AI/ML, NLP, LLMs knowledge.

Responsibilities

• Design, develop, and deploy enterprise AI/ML solutions on GCP.
• Build Generative AI apps using LLMs, NLP, RAG, and Agentic AI.
• Design scalable AI architectures integrated with enterprise systems.

Skills

Google Cloud Platform (GCP)

Python

AI/ML engineering

NLP

LLMs

RAG architectures

Agentic AI

MLOps

LLMOps

APIs & microservices

Tools

REST APIs

Microservices

Job description

Job Type: Long-Term Contract

Location: Charlotte, NC / St. Louis, MO (Hybrid)

Experience: 12+ Years

Job Summary

We are seeking an experienced Senior AI Engineer with strong expertise in Google Cloud Platform (GCP), AI/ML engineering, and enterprise architecture. The ideal candidate will have hands-on experience designing, developing, and deploying scalable AI solutions using Python, Natural Language Processing (NLP), Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Agentic AI.

The candidate will be responsible for building enterprise-grade AI applications, designing cloud-based AI architectures, and implementing reliable MLOps and LLMOps practices. This role requires a combination of strong technical development skills and architectural expertise to deliver secure, scalable, and high-performance AI solutions aligned with business objectives.

Key Responsibilities

• Design, develop, and deploy enterprise AI/ML solutions using Python and Google Cloud Platform (GCP).
• Build and optimize Generative AI applications using LLMs, NLP, RAG pipelines, and Agentic AI frameworks.
• Design and implement scalable AI architectures that integrate with enterprise applications, databases, and business workflows.
• Develop RAG solutions using vector search, embeddings, semantic search, and enterprise knowledge sources.
• Implement AI agents, workflow automation, tool integration, and intelligent decision-support capabilities.
• Build and maintain data pipelines and processing workflows to support AI/ML applications.
• Implement MLOps and LLMOps practices for model deployment, monitoring, evaluation, versioning, and lifecycle management.
• Leverage GCP services and cloud-native technologies to build scalable, reliable, and cost-effective AI applications.
• Develop and integrate microservices, REST APIs, and enterprise system interfaces.
• Implement security controls, Identity and Access Management (IAM), data protection, and governance practices.
• Establish CI/CD pipelines, automated testing, and deployment processes for AI applications.
• Collaborate with data engineers, cloud architects, software developers, and business stakeholders to define technical requirements and deliver solutions.
• Monitor AI application performance, troubleshoot production issues, and optimize reliability, latency, and cost.
• Provide technical leadership, architecture guidance, and best practices for enterprise AI engineering initiatives.

Required Skills

• 12+ years of experience in IT, software engineering, AI/ML engineering, or a related technical field.
• Strong hands-on experience with Google Cloud Platform (GCP) and cloud-based application development.
• Advanced programming skills in Python.
• Experience with Artificial Intelligence, Machine Learning, NLP, Generative AI, and Large Language Models (LLMs).
• Strong understanding of RAG architectures, Agentic AI, vector search, embeddings, and prompt engineering.
• Experience designing enterprise AI solutions and scalable cloud architectures.
• Knowledge of MLOps and LLMOps, including deployment, monitoring, evaluation, and lifecycle management.
• Experience developing APIs, microservices, and enterprise application integrations.
• Understanding of cloud security, IAM, data governance, and production deployment practices.
• Strong problem-solving, system design, communication, and technical leadership skills.