AI/ML Solutions Architect LLMs & Google Cloud AI

  • Atlanta, GA
  • Posted 8 hours ago | Updated 1 hour ago

Overview

Hybrid
$80 - $100
Contract - Independent
Contract - W2
Contract - 36 Month(s)
Able to Provide Sponsorship

Skills

Google Cloud
Google Cloud Platform
DevOps
Continuous Integration
Continuous Delivery
Machine Learning Operations (ML Ops)
Machine Learning (ML)
Generative Artificial Intelligence (AI)

Job Details

We are seeking a highly skilled LLM Engineer with deep experience in Google Cloud Platform (Google Cloud Platform) and Vertex AI to build, fine-tune, and deploy cutting-edge Generative AI applications. You will work on scalable machine learning pipelines, custom LLM integrations, and AI-based solutions across a variety of domains.

Key Responsibilities:

  • Design, develop, and deploy LLM-based applications using Vertex AI, Generative AI Studio, and Google Cloud Platform services

  • Fine-tune and optimize foundation models (PaLM, Gemini) for business-specific tasks

  • Build end-to-end ML pipelines: data ingestion, preprocessing, training, evaluation, and serving

  • Implement prompt engineering, embedding-based retrieval, and RAG pipelines

  • Integrate AI models into production systems with CI/CD and MLOps best practices

  • Collaborate with data scientists, ML engineers, and DevOps teams to deploy scalable solutions

  • Ensure model governance, monitoring, and responsible AI compliance

Required Skills:

  • 5+ years in ML/AI engineering with recent experience in LLMs and Generative AI

  • Strong hands-on experience with Vertex AI, BigQuery, GCS, and Cloud Functions

  • Experience fine-tuning LLMs (e.g., BERT, GPT, PaLM) using tools like Keras, PyTorch, or TFX

  • Familiarity with RAG (Retrieval Augmented Generation) and LangChain/LLamaIndex

  • Proficient in Python, REST APIs, and Google Cloud Platform SDKs

  • Working knowledge of MLOps on Google Cloud Platform

  • Bachelor's or Master s in Computer Science, AI/ML, or related field

Nice to Have:

  • Google Cloud Platform Professional Machine Learning Engineer certification

  • Experience with multi-modal models, Vision-Language tasks, or chatbot development

  • Exposure to GKE, Cloud Run, and Kubeflow Pipelines

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