Senior AI Engineer - Google AI & Generative Intelligence

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

resumes@navitassols.com