Responsibilities
- Design, develop, and deploy AI agents leveraging commercial LLMs including: Gemini (Google) GPT (OpenAI) Claude Sonnet (Anthropic)
- Gemini (Google)
- GPT (OpenAI)
- Claude Sonnet (Anthropic)
- Work with open-source and self-hosted LLMs such as: Mixtral (Mistral AI)
- Mixtral (Mistral AI)
- Build lightweight SLM-based solutions using: Phi-3 Gemma Mistral
- Phi-3
- Gemma
- Mistral
- Fine-tune and customize models using: Vertex AI Tuning Hugging Face Transformers PEFT methods including LoRA and QLoRA
- Vertex AI Tuning
- Hugging Face Transformers
- PEFT methods including LoRA and QLoRA
- Utilize frameworks such as: PyTorch TensorFlow JAX
- PyTorch
- TensorFlow
- JAX
- Perform synthetic data generation and model evaluations using: HELM lm-evaluation-harness Custom benchmarking frameworks
- HELM
- lm-evaluation-harness
- Custom benchmarking frameworks
- Design AI-powered workflows integrated with: Google Workspace Google Docs Sheets Drive Gmail Meet BigQuery Lakehouse platforms
- 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
- 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
- Vertex AI
- GKE (Google Kubernetes Engine)
- Cloud Run
- Cloud Functions
- Vertex AI Vector Databases
- 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
- Build orchestration pipelines using: LangChain LlamaIndex LangGraph
- LangChain
- LlamaIndex
- LangGraph
- Develop multi-agent AI systems using: Semantic Kernel LangGraph
- Semantic Kernel
- LangGraph
- Manage prompt engineering and observability using: LangSmith PromptLayer
- LangSmith
- PromptLayer
- Deploy models locally using Ollama and at scale using vLLM
- Track experiments using: MLflow Weights & Biases
- MLflow
- Weights & Biases
- Manage source control with Git
- Build Retrieval-Augmented Generation (RAG) systems using: Vertex AI Vector DB ChromaDB
- Vertex AI Vector DB
- ChromaDB
- Design enterprise semantic search and knowledge retrieval architectures
- Develop scalable RESTful APIs using: FastAPI (Python) Express.js (Node.js)
- FastAPI (Python)
- Express.js (Node.js)
- Manage APIs using: MuleSoft Apigee
- MuleSoft
- Apigee
- Develop modern AI-driven user interfaces using: React Angular Material-UI
- React
- Angular
- Material-UI
- Collaborate on UI/UX workflows and prototyping using Figma
- Perform LLM and RAG evaluations using: RAGAS DeepEval LangSmith Evaluators
- RAGAS
- DeepEval
- LangSmith Evaluators
- Create unit tests using pytest
- Monitor model performance and hallucination detection
- Track AI infrastructure costs using: OpenMeter Custom dashboards
- OpenMeter
- Custom dashboards
- Deploy AI systems using: Kubernetes Google GKE
- Kubernetes
- Google GKE
- Build CI/CD pipelines using: GitHub Actions GitLab CI
- GitHub Actions
- GitLab CI
- Support: Cloud deployments Hybrid deployments Edge AI inference environments
- Cloud deployments
- Hybrid deployments
- 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
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