Senior Generative AI Engineer (Java/Google Cloud Platform/LLM/RAG)

*Only W2 profiles*

Title: Senior Generative AI Engineer
Location: Remote
Direct client

We are seeking an experienced AI Engineer with a strong background in Java development and Google Cloud Platform (Google Cloud Platform). The ideal candidate will design, develop, and deploy AI/ML-powered applications, build scalable cloud-native solutions, and collaborate with cross-functional teams to deliver innovative AI products.

Key Responsibilities

• Design, develop, and deploy AI/ML solutions using modern cloud-native architectures.
• Build and maintain backend services and APIs using Java and related frameworks.
• Develop and integrate Generative AI, LLM, NLP, and machine learning applications.
• Deploy and manage AI workloads on Google Cloud Platform (Google Cloud Platform).
• Work with data engineers, architects, and business stakeholders to define AI use cases and technical solutions.
• Implement MLOps best practices for model deployment, monitoring, and maintenance.
• Optimize AI applications for performance, scalability, reliability, and security.
• Integrate AI services with enterprise applications and data platforms.
• Participate in architecture reviews, code reviews, and technical design discussions.

Required Qualifications

• Bachelor''''s or Master''''s degree in Computer Science, Engineering, or related field.
• 5+ years of software development experience with Java.
• 2+ years of hands-on experience building AI/ML or Generative AI solutions.
• Strong experience with:
  • Java 8/11/17+
  • Spring Boot, Microservices
  • REST APIs
  • Google Cloud Platform (Google Cloud Platform)
• Experience with Google Cloud Platform services such as:
  • Vertex AI
  • BigQuery
  • Cloud Run
  • Cloud Functions
  • GKE (Google Kubernetes Engine)
  • Pub/Sub
  • Cloud Storage
• Experience with Python for AI/ML development.
• Knowledge of machine learning frameworks such as TensorFlow, PyTorch, LangChain, or similar.
• Experience working with vector databases and Retrieval-Augmented Generation (RAG).
• Familiarity with Docker, Kubernetes, and CI/CD pipelines.
• Strong understanding of software engineering best practices and design patterns.