Senior AI Engineer Google AI & Generative Intelligence (Paramus, NJ)

Job Title:

Senior AI Engineer•

Google AI & Generative Intelligence Location:

Paramus, NJ 07652/ Hybrid Contract length: 6•month contract to hire

Pay:

$88/hr•$93/hr Senior AI Engineer•Google AI & Generative Intelligence We are seeking a highly experienced Senior AI Engineer with deep expertise in Google AI technologies and Generative AI solutions. The ideal candidate will bring 10-15 years of overall software engineering experience, with at least 4+ years focused specifically on designing, developing, deploying, and monitoring production-grade Generative AI systems. This role requires strong expertise across the Google AI ecosystem, including Google Workspace integrations, Google Agent Development Kit (ADK), Vertex AI, and modern LLM/SLM frameworks. The ideal candidate will also have hands-on experience with cloud-native infrastructure, MLOps, multi-agent systems, and enterprise AI deployments. Key Responsibilities 1. Large & Small Language Model Engineering Design, develop, and deploy AI agents leveraging commercial LLMs such as Gemini (Google), GPT (OpenAI), and Claude Sonnet (Anthropic) for high-performance, multimodal, and large-context applications. Work with open-source and self-hosted models including Mixtral (Mistral AI). Architect SLM-based solutions using lightweight models such as Phi-3, Gemma, and Mistral for resource-constrained environments. Lead model fine-tuning and customization using: Vertex AI Tuning Hugging Face Transformers PEFT methodologies including LoRA and QLoRA Utilize PyTorch, TensorFlow, and/or JAX for experimentation and model development. Generate synthetic datasets and evaluate models using HELM, lm-evaluation-harness, and custom benchmarking frameworks. 2. Google AI & Workspace Integration Design and implement AI-powered solutions deeply integrated with Google Workspace applications, including Docs, Sheets, Drive, Gmail, and Meet. Build intelligent agents and workflows using Google Agent Development Kit (ADK). Utilize Google AI Studio and VS Code for AI application development and prototyping. Leverage Google Cloud Platform (GCP) services, including: Vertex AI Google Kubernetes Engine (GKE) Cloud Run Cloud Functions Vertex AI Vector Search BigQuery and Lakehouse architectures 3. Solution Design & Planning Lead requirements gathering and technical documentation using Confluence. Create system architecture diagrams and AI workflows using Lucidchart. Design UI/UX prototypes in Figma for AI-powered applications. Manage project delivery and sprint planning through Jira. Oversee data preparation, transformation, and organization for AI/ML workflows. Conduct exploratory data analysis using Jupyter Notebooks and pandas. Utilize Hugging Face Model Hub for model evaluation and selection. 4. Development Frameworks & Tools Build and orchestrate LLM/SLM applications using: LangChain LlamaIndex LangGraph Develop multi-agent systems using Semantic Kernel and LangGraph. Manage prompts and evaluations using LangSmith and PromptLayer. Deploy models locally using Ollama or at scale with vLLM. Track experiments and performance metrics with MLflow or Weights & Biases. Manage source control and collaboration using Git. 5. Vector Databases & RAG Architectures Design and implement semantic search and Retrieval-Augmented Generation (RAG) pipelines using: Vertex AI Vector Search ChromaDB Architect enterprise-grade RAG systems optimized for scalable knowledge retrieval. 6. Backend Development Develop robust RESTful APIs using: FastAPI (Python) Express.js (Node.js) Secure and manage APIs using MuleSoft and Apigee. 7. Frontend Development Build modern user interfaces using React or Angular. Utilize Material-UI for scalable and accessible UI components. Collaborate on UI/UX workflows and prototypes using Figma. 8. Development Tools & Code Quality Write and debug code using VS Code with GitHub Copilot integrations. Manage repositories using GitHub or GitLab.

Enforce code quality standards using:

SonarQube ESLint Pylint 9. Testing & Quality Assurance Conduct LLM and RAG pipeline evaluations using: RAGAS DeepEval LangSmith Evaluators Perform hallucination detection and prompt validation. Write and execute unit tests using pytest. Implement custom evaluation metrics to ensure output reliability and quality. 10. Deployment & Infrastructure Orchestrate containerized environments using Kubernetes and GKE. Build and maintain CI/CD pipelines using GitHub Actions or GitLab CI. Support cloud, hybrid, on-premise, and edge deployment environments. 11. LLM Monitoring & Observability Monitor AI model performance using LangSmith and Weights & Biases. Track infrastructure utilization and AI costs using OpenMeter and custom dashboards. Establish continuous evaluation pipelines to ensure ongoing model quality and reliability. Implement end-to-end observability for AI applications and infrastructure. Required Qualifications 10-15 years of overall software engineering experience. 5+ years of hands-on experience in Generative AI, including: LLMs SLMs RAG architectures Multi-agent systems Deep expertise within the Google AI ecosystem: Gemini Vertex AI Google ADK Google AI Studio Google Workspace integrations Strong proficiency in Python and familiarity with Node.js. Extensive experience building cloud-native solutions on GCP.

Proven expertise in:

LoRA / QLoRA / PEFT Model evaluation frameworks MLOps and CI/CD for AI systems Production AI deployments Experience building multi-agent architectures using Semantic Kernel and/or LangGraph. Preferred Qualifications Google Cloud certifications such as: Professional ML Engineer Professional Cloud Architect Contributions to open-source AI/ML projects. Experience with edge AI deployments and hybrid cloud-edge inference. Familiarity with synthetic data generation pipelines. Prior experience mentoring junior AI/ML engineers and interns.