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.

AI / LLM Engineering

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.

DevOps / MLOps

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.

Security & Identity

Secure AI application front-ends and user interfaces by integrating them with enterprise Single Sign-On (SSO) and Multi-Factor Authentication (MFA)

OIDC

OAuth 2.0

SAML

Integrate AI agent frameworks and service accounts with enterprise IGA platforms

• Access provisioning
• Entitlements
• Compliance auditing

Collaboration

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

Qualifications Education

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

Experience

• 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
  • LangChain
  • LangGraph
  • Model training
  • Fine-tuning
  • Evaluation
  • Deployment
  • Strong coding skills in Python.
  • Experience with modern MLOps practices.
  • Service accounts
  • IAM roles
  • Secret management tools
  • GCP IAM
  • Vertex AI security