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NavitasPartners
AI

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

NavitasPartners · Newark, NJ, US

Actively hiring Posted about 1 month ago

Responsibilities

  • Design, develop, and deploy AI agents leveraging commercial LLMs including: Gemini (Google) GPT (OpenAI) Claude Sonnet (Anthropic)
  • Gemini (Google)
  • GPT (OpenAI)
  • Claude Sonnet (Anthropic)
  • Work with open-source and self-hosted LLMs such as: Mixtral (Mistral AI)
  • Mixtral (Mistral AI)
  • Build lightweight SLM-based solutions using: Phi-3 Gemma Mistral
  • Phi-3
  • Gemma
  • Mistral
  • Fine-tune and customize models using: Vertex AI Tuning Hugging Face Transformers PEFT methods including LoRA and QLoRA
  • Vertex AI Tuning
  • Hugging Face Transformers
  • PEFT methods including LoRA and QLoRA
  • Utilize frameworks such as: PyTorch TensorFlow JAX
  • PyTorch
  • TensorFlow
  • JAX
  • Perform synthetic data generation and model evaluations using: HELM lm-evaluation-harness Custom benchmarking frameworks
  • HELM
  • lm-evaluation-harness
  • Custom benchmarking frameworks
  • Design AI-powered workflows integrated with: Google Workspace Google Docs Sheets Drive Gmail Meet BigQuery Lakehouse platforms
  • 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
  • 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
  • Vertex AI
  • GKE (Google Kubernetes Engine)
  • Cloud Run
  • Cloud Functions
  • Vertex AI Vector Databases
  • 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
  • Build orchestration pipelines using: LangChain LlamaIndex LangGraph
  • LangChain
  • LlamaIndex
  • LangGraph
  • Develop multi-agent AI systems using: Semantic Kernel LangGraph
  • Semantic Kernel
  • LangGraph
  • Manage prompt engineering and observability using: LangSmith PromptLayer
  • LangSmith
  • PromptLayer
  • Deploy models locally using Ollama and at scale using vLLM
  • Track experiments using: MLflow Weights & Biases
  • MLflow
  • Weights & Biases
  • Manage source control with Git
  • Build Retrieval-Augmented Generation (RAG) systems using: Vertex AI Vector DB ChromaDB
  • Vertex AI Vector DB
  • ChromaDB
  • Design enterprise semantic search and knowledge retrieval architectures
  • Develop scalable RESTful APIs using: FastAPI (Python) Express.js (Node.js)
  • FastAPI (Python)
  • Express.js (Node.js)
  • Manage APIs using: MuleSoft Apigee
  • MuleSoft
  • Apigee
  • Develop modern AI-driven user interfaces using: React Angular Material-UI
  • React
  • Angular
  • Material-UI
  • Collaborate on UI/UX workflows and prototyping using Figma
  • Perform LLM and RAG evaluations using: RAGAS DeepEval LangSmith Evaluators
  • RAGAS
  • DeepEval
  • LangSmith Evaluators
  • Create unit tests using pytest
  • Monitor model performance and hallucination detection
  • Track AI infrastructure costs using: OpenMeter Custom dashboards
  • OpenMeter
  • Custom dashboards
  • Deploy AI systems using: Kubernetes Google GKE
  • Kubernetes
  • Google GKE
  • Build CI/CD pipelines using: GitHub Actions GitLab CI
  • GitHub Actions
  • GitLab CI
  • Support: Cloud deployments Hybrid deployments Edge AI inference environments
  • Cloud deployments
  • Hybrid deployments
  • Edge AI inference environments

Basic 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
  • 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
  • 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
  • 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

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