Senior AI Engineer - Agentic AI & RAG

We are looking for an AI Engineer with deep experience in building agent-based AI applications. This role is ideal for someone passionate about pushing the boundaries of applied AI, particularly in Retrieval-Augmented Generation (RAG), and developing production-grade intelligent systems that are secure, governed, and enterprise-ready.

• Design, develop, and deploy agent-based AI systems using LLMs.
• Build and scale Retrieval-Augmented Generation (RAG) pipelines for real-time and offline inference.
• Develop and optimize training workflows for fine-tuning and adapting models to domain-specific tasks.
• Collaborate with cross-functional teams to integrate knowledge bases into agent frameworks.
• Drive best practices in AI Engineering, model lifecycle management, and production deployment on Google Cloud (GCP).
• Monitor, evaluate, and improve model performance post-deployment on Google Cloud.

• Implement version control strategies using Git, manage code repositories, and ensure best practices in code management.
• Develop and manage CI/CD pipelines using GitHub Actions, Jenkins, or other relevant tools to streamline deployment and updates.

• Secure AI application front-ends and user interfaces by integrating them with enterprise Single Sign-On (SSO) and Multi-Factor Authentication (MFA) using:
  • OIDC
  • OAuth 2.0
  • SAML
• Integrate AI agent frameworks and service accounts with enterprise IGA platforms to automate:
  • Access provisioning
  • Entitlements
  • Compliance auditing

• Communicate technical findings and insights to non-technical stakeholders.
• Participate in technical discussions and contribute to strategic planning.

• Master s / Bachelor s degree in:
  • Computer Science
  • Artificial Intelligence
  • Machine Learning
  • Or a related field

• 10+ years of overall IT experience.
• 5+ years of AI/ML Engineering experience, with a strong focus on LLM-based applications.
• Proven experience building agent-based applications using Gemini, OpenAI, or similar models.
• Deep understanding of:
  • RAG systems
  • Vector databases
  • Knowledge retrieval strategies
• Hands-on experience with:
  • LangChain
  • LangGraph
• Solid background in:
  • Model training
  • Fine-tuning
  • Evaluation
  • Deployment
• Strong coding skills in Python.
• Experience with modern MLOps practices.
• Experience managing:
  • Service accounts
  • IAM roles
  • Secret management tools
  • GCP IAM
  • Vertex AI security