Senior AI Engineer - Google AI & Generative Intelligence - 26-05877

• Design, develop, and deploy AI agents leveraging commercial LLMs including:

• Gemini (Google)
• GPT (OpenAI)
• Claude Sonnet (Anthropic)

• Work with open-source and self-hosted LLMs such as:

• Mixtral (Mistral AI)

• Build lightweight SLM-based solutions using:

• Phi-3
• Gemma
• Mistral

• Fine-tune and customize models using:

• Vertex AI Tuning
• Hugging Face Transformers
• PEFT methods including LoRA and QLoRA

• Utilize frameworks such as:

• PyTorch
• TensorFlow
• JAX

• Perform synthetic data generation and model evaluations using:

• HELM
• lm-evaluation-harness
• Custom benchmarking frameworks

Google AI & Workspace Integration

• Design AI-powered workflows integrated with:

• Google Workspace
• Google Docs
• Sheets
• Drive
• Gmail
• Meet
• BigQuery
• Lakehouse platforms

• Develop intelligent AI agents using Google Agent Development Kit (ADK)
• Utilize:

• Google AI Studio
• VS Code

• Work extensively with Google Cloud Platform (GCP) services:

• Vertex AI
• GKE (Google Kubernetes Engine)
• Cloud Run
• Cloud Functions
• Vertex AI Vector Databases

AI Solution Design & Planning

• Lead requirements gathering and technical documentation using Confluence
• Create AI workflows and system architecture diagrams using Lucidchart
• Design UI/UX prototypes using Figma
• Manage Agile sprint planning and delivery using Jira
• Prepare, clean, and organize enterprise datasets for AI/ML workflows
• Conduct data analysis using Jupyter Notebooks and pandas
• Utilize Hugging Face Model Hub for model research and selection

Development Frameworks & AI Tooling

• Build orchestration pipelines using:

• LangChain
• LlamaIndex
• LangGraph

• Develop multi-agent AI systems using:

• Semantic Kernel
• LangGraph

• Manage prompt engineering and observability using:

• LangSmith
• PromptLayer

• Deploy models locally using Ollama and at scale using vLLM
• Track experiments using:

• MLflow
• Weights & Biases

• Manage source control with Git

Vector Databases & RAG Architecture

• Build Retrieval-Augmented Generation (RAG) systems using:

• Vertex AI Vector DB
• ChromaDB

• Design enterprise semantic search and knowledge retrieval architectures

Backend Development

• Develop scalable RESTful APIs using:

• FastAPI (Python)
• Express.js (Node.js)

• Manage APIs using:

• MuleSoft
• Apigee

Frontend Development

• Develop modern AI-driven user interfaces using:

• React
• Angular
• Material-UI

• Collaborate on UI/UX workflows and prototyping using Figma

Testing, Quality & Observability

• Perform LLM and RAG evaluations using:

• RAGAS
• DeepEval
• LangSmith Evaluators

• Create unit tests using pytest
• Monitor model performance and hallucination detection
• Track AI infrastructure costs using:

• OpenMeter
• Custom dashboards

Deployment & Infrastructure

• Deploy AI systems using:

• Kubernetes
• Google GKE

• Build CI/CD pipelines using:

• GitHub Actions
• GitLab CI

• Support:

• Cloud deployments
• Hybrid deployments
• Edge AI inference environments

Do you have experience in Test Automation Development (Quality assurance practices)?, We are seeking a highly experienced Senior AI Engineer with strong expertise in Google AI technologies, Generative AI, and cloud-native AI application development. The ideal candidate will bring 10-15 years of software engineering experience, including 5+ years focused on Artificial Generative Intelligence, building scalable AI systems, LLM/SLM applications, RAG architectures, and multi-agent solutions in production environments.

This role requires deep hands-on experience with the Google AI ecosystem including Gemini, Vertex AI, Google Agent Development Kit (ADK), Google AI Studio, and Google Workspace integrations., * 10-15 years of overall software engineering experience

• 5+ years of hands-on Generative AI experience
• Strong expertise with:

• Gemini
• Vertex AI
• Google ADK
• Google AI Studio
• Google Workspace integrations

• Strong Python development experience
• Familiarity with Node.js
• Experience with:

• RAG systems
• Multi-agent AI architectures
• LLM/SLM fine-tuning
• LoRA / QLoRA / PEFT
• AI evaluation frameworks

• Strong cloud-native development experience on GCP
• Experience with MLOps and AI CI/CD pipelines, * Google Cloud certifications such as:

• Professional ML Engineer
• Professional Cloud Architect

• Experience contributing to open-source AI/ML projects
• Experience with edge AI and hybrid cloud deployments
• Experience building synthetic data generation pipelines
• Prior mentoring or leadership experience within AI/ML teams