What are the responsibilities and job description for the Senior AI Engineer Google AI & Generative Intelligence position at Sincera Technologies, Inc.?
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:
- 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.
- 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 (Google Cloud Platform) services including:
Vertex AI for ML model training, tuning, and deployment
Vertex AI Vector DBs for semantic search and retrieval
- 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.
- 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.
- 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.
- Backend Development
Develop robust RESTful APIs using FastAPI (Python) or Express.js (Node.js).
Manage and secure APIs using Mulesoft, Apigee.
- 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.
- 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.
- 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.
- Deployment & Infrastructure
Support on-premise, cloud (Google Cloud Platform/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, Google AI Studio, and Google Workspace integrations.
- Proficiency in Python (primary) and familiarity with Node.js.
- Strong background in cloud-native development on Google Cloud Platform.
- Experience with multi-agent AI architectures using Semantic Kernel, or LangGraph.
Salary : $120,000 - $200,000