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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior AI Engineer - Google AI & Generative Intelligence - 26-05877 - **Company:** Navitas, Inc. - **Location:** Newark, NJ, United States - **Experience:** Expert - **Salary:** $124,800.0 - **Contract:** Temporary contract - **Skills:** Application Programming Interfaces (APIs), Agile Methodology, Artificial Intelligence, Data Analysis, Confluence, JIRA, Automation of Tests, Unit Testing, BigQuery, Cloud Computing, Cloud Engineering, Google Docs, Github, Design of User Interfaces, Python (Programming Language), Node.Js, Open Source Technology, Performance Tuning, Scrum Methodology, Cloud Services, Lucidchart, Tensorflow, Search Technologies, Software Engineering, Systems Architecture, AI Infrastructure, Jupyter Notebook, Google Cloud, ReactJS, Large Language Models, Express.js, Multi-Agent Systems, Prompt Engineering, Generative AI, HybridCloud, Apigee, Backend, Git, Fastapi, Pytest, AngularJS, Gitlab-ci, Kubernetes, HuggingFace, Figma, Machine Learning Operations, Gsuite, Front End Software Development, Restful APIs, GPT, Software Version Control, Automation Anywhere, Mulesoft, Data Generation - **Published:** May 21, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=a3b58960d8746e09 ## About the Role 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 ## Description * 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 ## Related Videos - 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