Senior AI Engineer - Google AI Generative Intelligence - 26-05877
Actively Hiring
Full-time Posted 2 months ago
Responsibilities
- check_circle Design, develop, and deploy AI agents leveraging commercial LLMs including: Gemini (Google) GPT (OpenAI) Claude Sonnet (Anthropic)
- check_circle Gemini (Google)
- check_circle GPT (OpenAI)
- check_circle Claude Sonnet (Anthropic)
- check_circle Work with open-source and self-hosted LLMs such as: Mixtral (Mistral AI)
- check_circle Mixtral (Mistral AI)
- check_circle Build lightweight SLM-based solutions using: Phi-3 Gemma Mistral
- check_circle Phi-3
- check_circle Gemma
- check_circle Mistral
- check_circle Fine-tune and customize models using: Vertex AI Tuning Hugging Face Transformers PEFT methods including LoRA and QLoRA
- check_circle Vertex AI Tuning
- check_circle Hugging Face Transformers
- check_circle PEFT methods including LoRA and QLoRA
- check_circle Utilize frameworks such as: PyTorch TensorFlow JAX
- check_circle PyTorch
- check_circle TensorFlow
- check_circle JAX
- check_circle Perform synthetic data generation and model evaluations using: HELM lm-evaluation-harness Custom benchmarking frameworks
- check_circle HELM
- check_circle lm-evaluation-harness
- check_circle Custom benchmarking frameworks
- check_circle Design AI-powered workflows integrated with: Google Workspace Google Docs Sheets Drive Gmail Meet BigQuery Lakehouse platforms
- check_circle Google Workspace
- check_circle Google Docs
- check_circle Sheets
- check_circle Drive
- check_circle Gmail
- check_circle Meet
- check_circle BigQuery
- check_circle Lakehouse platforms
- check_circle Develop intelligent AI agents using Google Agent Development Kit (ADK)
- check_circle Utilize: Google AI Studio VS Code
- check_circle Google AI Studio
- check_circle VS Code
- check_circle Work extensively with Google Cloud Platform (GCP) services: Vertex AI GKE (Google Kubernetes Engine) Cloud Run Cloud Functions Vertex AI Vector Databases
- check_circle Vertex AI
- check_circle GKE (Google Kubernetes Engine)
- check_circle Cloud Run
- check_circle Cloud Functions
- check_circle Vertex AI Vector Databases
- check_circle Lead requirements gathering and technical documentation using Confluence
- check_circle Create AI workflows and system architecture diagrams using Lucidchart
- check_circle Design UI/UX prototypes using Figma
- check_circle Manage Agile sprint planning and delivery using Jira
- check_circle Prepare, clean, and organize enterprise datasets for AI/ML workflows
- check_circle Conduct data analysis using Jupyter Notebooks and pandas
- check_circle Utilize Hugging Face Model Hub for model research and selection
- check_circle Build orchestration pipelines using: LangChain LlamaIndex LangGraph
- check_circle LangChain
- check_circle LlamaIndex
- check_circle LangGraph
- check_circle Develop multi-agent AI systems using: Semantic Kernel LangGraph
- check_circle Semantic Kernel
- check_circle LangGraph
- check_circle Manage prompt engineering and observability using: LangSmith PromptLayer
- check_circle LangSmith
- check_circle PromptLayer
- check_circle Deploy models locally using Ollama and at scale using vLLM
- check_circle Track experiments using: MLflow Weights & Biases
- check_circle MLflow
- check_circle Weights & Biases
- check_circle Manage source control with Git
- check_circle Build Retrieval-Augmented Generation (RAG) systems using: Vertex AI Vector DB ChromaDB
- check_circle Vertex AI Vector DB
- check_circle ChromaDB
- check_circle Design enterprise semantic search and knowledge retrieval architectures
- check_circle Develop scalable RESTful APIs using: FastAPI (Python) Express.js (Node.js)
- check_circle FastAPI (Python)
- check_circle Express.js (Node.js)
- check_circle Manage APIs using: MuleSoft Apigee
- check_circle MuleSoft
- check_circle Apigee
- check_circle Develop modern AI-driven user interfaces using: React Angular Material-UI
- check_circle React
- check_circle Angular
- check_circle Material-UI
- check_circle Collaborate on UI/UX workflows and prototyping using Figma
- check_circle Perform LLM and RAG evaluations using: RAGAS DeepEval LangSmith Evaluators
- check_circle RAGAS
- check_circle DeepEval
- check_circle LangSmith Evaluators
- check_circle Create unit tests using pytest
- check_circle Monitor model performance and hallucination detection
- check_circle Track AI infrastructure costs using: OpenMeter Custom dashboards
- check_circle OpenMeter
- check_circle Custom dashboards
- check_circle Deploy AI systems using: Kubernetes Google GKE
- check_circle Kubernetes
- check_circle Google GKE
- check_circle Build CI/CD pipelines using: GitHub Actions GitLab CI
- check_circle GitHub Actions
- check_circle GitLab CI
- check_circle Support: Cloud deployments Hybrid deployments Edge AI inference environments
- check_circle Cloud deployments
- check_circle Hybrid deployments
- check_circle Edge AI inference environments
Basic qualifications
- 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
- 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
- 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
Preferred qualifications
- Google Cloud certifications such as: Professional ML Engineer Professional Cloud Architect
- 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
Tags & Focus Areas
Ai Ai Engineer Generative Ai