Senior AI Engineer - Google AI & Generative Intelligence - 26-05877
hace 2 días
Newark
Job DescriptionSenior 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 frameworksGoogle 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 DatabasesAI 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 selectionDevelopment 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 GitVector Databases & RAG Architecture, • Build Retrieval-Augmented Generation (RAG) systems using:, • Vertex AI Vector DB, • ChromaDB, • Design enterprise semantic search and knowledge retrieval architecturesBackend Development, • Develop scalable RESTful APIs using:, • FastAPI (Python), • Express.js (Node.js), • Manage APIs using:, • MuleSoft, • ApigeeFrontend Development, • Develop modern AI-driven user interfaces using:, • React, • Angular, • Material-UI, • Collaborate on UI/UX workflows and prototyping using FigmaTesting, 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 dashboardsDeployment & 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 environmentsRequired 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 pipelinesPreferred 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