AI Engineer

Role Description

AI Engineer

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.

Responsibilities

Design, develop and deploy agent-based AI systems using LLMs

Build and scale Retrieval-Augmented Generation 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 base into agent frameworks.

Drive best practices in AI Engineering, model lifecycle management, and production deployment on Google Cloud (GCP)

Implement version control strategies using Git, manage code repositories and ensure best practices in code management.

Develop, manage CI/CD pipelines using, Github Actions, Jenkins or other relevant tools to streamline deployment and updates.

Monitor, evaluate, and improve model performance post- deployment on Google Cloud.

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 and SAML.

Integrate AI agent frameworks and service accounts with enterprise IGA platforms to automate access provisioning, entitlements, and compliance auditing.[1]

Communicate technical findings and insights to non-technical stakeholders.

Participate in technical discussions and contribute to strategic planning.

QualificationsEducation

Master's / Bachelors in Computer Science, Artificial Intelligence, Machine Learning, or related field.

Experience

5+ years of experience in AI/ML engineering, with a strong focus on LLM-based applications. At least 10+ years of experience in IT overall.

Proven experience in building agent-based applications using Gemini, OpenAI or similar models.

Deep understanding of RAG systems, vector databases, and knowledge retrieval strategies.

Hands-on experience with LangChain and LangGraph frameworks.

Solid background in model training, fine-tuning, evaluation and deployment.

Strong coding skills in Python and experience with modern MLOps practices.

Experience managing service accounts, IAM roles, and secret management tools within cloud environments (specifically GCP IAM and Vertex AI security).

Nice To Have

Familiarity with frontend integration of AI agents (Eg. using Angular, Mesop, Streamlit or similar frameworks).

Experience with Google Cloud services like BigQuery, Dataflow, Vertex AI, Agent Builder.

Exposure to Angular framework

Containerization using Docker and Kubernetes to productionize and host models

Deep understanding of OIDC, OAuth 2.0, SAML, and federated identity concepts.

Familiarity with enterprise IGA concepts (contextual access, role based access controls, user provisioning lifecycl

Skills

devops,artificial intelligence,ai system,cloud computing,google cloud services,