Senior AI Engineer

Role Overview

We are seeking a highly experienced Senior AI Engineer with deep expertise in Google AI technologies, Generative AI. The ideal candidate brings 10–15 years of broad software engineering experience, with the last 2+ years focused exclusively on Artificial Generative Intelligence, including designing, building, deploying, and monitoring production-grade AI systems. This role demands mastery of the Google ecosystem — including Google Workspace, Google Agent Development Kit (ADK), and Vertex AI — alongside a strong command of modern LLM/SLM frameworks, cloud-native infrastructure, and MLOps best practices.

Key Responsibilities

1. AI Engineering

• Design, develop, and deploy Agents leveraging commercial LLMs such as Gemini (Google), GPT (OpenAI), and Claude Sonnet (Anthropic) for high-performance, large-context, and multimodal tasks.

2. Google AI & Workspace Integration

• Lead the design and implementation of AI-powered solutions deeply integrated with Google Workspace (Docs, Sheets, Drive, Gmail, Meet), Big Query and Lakehouse.
• Architect and build intelligent agents and workflows using Google Agent Development Kit (ADK).
• Leverage Google AI Studio as the primary IDE, VSCode for AI application development and prototyping.
• Utilize Google Cloud Platform (GCP) services including: Vertex AI for ML model training, tuning, and deployment Vertex AI Vector DBs for semantic search and retrieval

3. Design & Planning

• Lead requirements gathering using Confluence for documentation and team collaboration.
• Create detailed system architecture diagrams and AI workflows using Lucidchart.
• Manage project delivery and sprint planning using Jira.

4. Development Frameworks & Tools

• Orchestrate LLM/SLM applications using LangChain, LlamaIndex, and LangGraph.
• Build multi-agent systems with Semantic Kernel, and LangGraph.
• Manage and optimize prompts using LangSmith and PromptLayer.
• Manage code and data versioning with Git.

5. Vector Databases & Semantic Search

• Implement semantic search and Retrieval-Augmented Generation (RAG) pipelines using Vertex AI Vector DBs and ChromaDB.
• Design and optimize end-to-end RAG architectures for enterprise-grade knowledge retrieval.

6. Backend Development

• Develop robust RESTful APIs using FastAPI (Python) or Express.js (Node.js).
• Manage and secure APIs using Mulesoft, Apigee.

7. Frontend Development

• Drupal Content Management System (PHP Backend + JS Frontend) - Drupal 10.4,PHP 8.1
• Build modern user interfaces using React or Angular.
• Utilize Material-UI for consistent, accessible, and modern UI components.
• OAuth2 authentication.

8. Development Tools & Code Quality

• Write and debug code in VS Code with Python and GitHub Copilot extensions.
• Manage source code with GitHub or GitLab.
• Enforce code quality and standards using SonarQube, ESLint, and Pylint.

9. Testing & Quality Assurance

• Conduct LLM-specific testing using RAGAS and DeepEval for LLM/RAG pipeline evaluation.
• Use LangSmith Evaluators for prompt testing and hallucination detection.
• Write and execute unit tests using pytest.
• Ensure output quality and reliability using LangChain Evaluators and custom metrics.

10. Deployment & Infrastructure

• Support on-premise, cloud (GCP/Vertex AI), and hybrid infrastructure deployments including edge devices for local inference.

Required Qualifications

• 10–15 years of overall software engineering experience.
• 3+ years of hands-on experience in Artificial Generative Intelligence, including LLMs, SLMs, RAG, and multi-agent systems.
• Deep expertise in Google AI ecosystem: Gemini, Vertex AI, Google ADK, and Google Workspace integrations.
• Proficiency in Python (primary) and familiarity with Node.js.
• Strong background in cloud-native development on GCP.
• Experience with multi-agent AI architectures using Semantic Kernel, or LangGraph