Summer Python Developer Google Jobs in New York (NOW HIRING)

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 ResponsibilitiesLarge & 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