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

Senior AI Engineer

• Google AI & Generative Intelligence
• 26-05877 NavitasPartners
• 4.0 Jersey City, NJ Job Details Temp-to-hire From $60 an hour 2 hours ago Qualifications AI models Software engineering Test Automation Development (Quality assurance practices) Kubernetes Generative models Research Machine learning cloud services Machine intelligence Model deployment Developing large-scale AI models DevOps automation MLOps Generative AI Full Job Description Senior AI Engineer

• Google AI & Generative Intelligence Job Title:

  Senior AI Engineer

•

Google AI & Generative Intelligence Location:

Paramus, New Jersey (Hybrid)

Duration:

6

Months Employment Type:

Contract-to-Hire Position Overview 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. Key Responsibilities Large & Small Language Model Engineering 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 Required 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 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 Preferred Qualifications 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 For more details reach at