Sr AI Engineer

Job Title: Senior AI Engineer – Google AI & Generative Intelligence
Duration: 6 Months (Temp-to-Hire)
Location: Paramus, NJ [Hybrid]
 
Role Overview
We are seeking a highly experienced Senior AI Engineer with deep expertise in Google AI technologies, Generative AI. The ideal candidate brings 10–15 years of broad software engineering experience, with the last 4+ years focused exclusively on Artificial Generative Intelligence, including designing, building, deploying, and monitoring production-grade AI systems. This role demands mastery of the Google ecosystem — including Google Workspace, Google Agent Development Kit (ADK), and Vertex AI — alongside a strong command of modern LLM/SLM frameworks, cloud-native infrastructure, and MLOps best practices.
 
Key Responsibilities
1. Large & Small Language Model Engineering

• Design, develop, and deploy Agents leveraging commercial LLMs such as Gemini (Google), GPT (OpenAI), and Claude Sonnet (Anthropic) for high-performance, large-context, and multimodal tasks.
• Work with open-source/self-hosted LLMs including Mixtral (Mistral AI).
• Architect and implement SLM-based solutions using lightweight models such as Phi-3 (Microsoft), Gemma (Google), and Mistral for resource-constrained environments.
• Lead fine-tuning and customization of models using Vertex AI Tuning, Hugging Face Transformers, and parameter-efficient fine-tuning (PEFT) methods including LoRA and QLoRA.
• Apply training frameworks such as PyTorch, TensorFlow, or JAX for model experimentation and development.
• Generate synthetic data and evaluate models using HELM, lm-evaluation-harness, and custom benchmarks.

 
2. Google AI & Workspace Integration

• Lead the design and implementation of AI-powered solutions deeply integrated with Google Workspace (Docs, Sheets, Drive, Gmail, Meet), Big Query and Lakehouse.
• Architect and build intelligent agents and workflows using Google Agent Development Kit (ADK).
• Leverage Google AI Studio as the primary IDE, VSCode for AI application development and prototyping.
• Utilize Google Cloud Platform (GCP) services including:
• Vertex AI for ML model training, tuning, and deployment
• GKE (Google Kubernetes Engine) for container orchestration
• Cloud Run for serverless deployment
• Cloud Functions for event-driven AI tasks
• Vertex AI Vector DBs for semantic search and retrieval

 
3. Design & Planning

• Lead requirements gathering using Confluence for documentation and team collaboration.
• Create detailed system architecture diagrams and AI workflows using Lucidchart.
• Design UI/UX prototypes in Figma for AI-powered application interfaces.
• Manage project delivery and sprint planning using Jira.
• Oversee data preparation and management: cleaning, transforming, and organizing data for AI/ML workflows.
• Conduct data analysis using Jupyter Notebooks and pandas for exploration and preprocessing.
• Leverage Hugging Face Model Hub for model comparison, selection, and download.

 
4. Development Frameworks & Tools

• Orchestrate LLM/SLM applications using LangChain, LlamaIndex, and LangGraph.
• Build multi-agent systems with Semantic Kernel, and LangGraph.
• Manage and optimize prompts using LangSmith and PromptLayer.
• Deploy models locally with Ollama or at scale with vLLM for efficient inference.
• Track experiments, metrics, and results with MLflow or Weights & Biases.
• Manage code and data versioning with Git.

 
5. Vector Databases & Semantic Search

• Implement semantic search and Retrieval-Augmented Generation (RAG) pipelines using Vertex AI Vector DBs and ChromaDB.
• Design and optimize end-to-end RAG architectures for enterprise-grade knowledge retrieval.

 
6. Backend Development

• Develop robust RESTful APIs using FastAPI (Python) or Express.js (Node.js).
• Manage and secure APIs using Mulesoft, Apigee.

 
7. Frontend Development

• Build modern user interfaces using React or Angular.
• Utilize Material-UI for consistent, accessible, and modern UI components.
• Prototype and plan UI/UX workflows using Figma.

 
8. Development Tools & Code Quality

• Write and debug code in VS Code with Python and GitHub Copilot extensions.
• Leverage GitHub Copilot for AI-assisted code suggestions and productivity.
• Manage source code with GitHub or GitLab.
• Enforce code quality and standards using SonarQube, ESLint, and Pylint.

 
9. Testing & Quality Assurance

• Conduct LLM-specific testing using RAGAS and DeepEval for LLM/RAG pipeline evaluation.
• Use LangSmith Evaluators for prompt testing and hallucination detection.
• Write and execute unit tests using pytest.
• Ensure output quality and reliability using LangChain Evaluators and custom metrics.

 
10. Deployment & Infrastructure

• Orchestrate containers at scale with Kubernetes (K8s), and Google GKE.
• Automate CI/CD pipelines using GitHub Actions or GitLab CI.
• Support on-premise, cloud (GCP/Vertex AI), and hybrid infrastructure deployments including edge devices for local inference.

 
11. LLM Monitoring & Observability

• Monitor LLM performance and usage with LangSmith and Weights & Biases.
• Track and optimize AI infrastructure costs using OpenMeter and custom dashboards.
• Set up continuous evaluation pipelines to ensure ongoing model quality and reliability.
• Monitor application and model performance end-to-end with LangSmith observability tools.

 
Required Qualifications:

• 10–15 years of overall software engineering experience.
• 5+ years of hands-on experience in Artificial Generative Intelligence, including LLMs, SLMs, RAG, and multi-agent systems.
• Deep expertise in Google AI ecosystem: Gemini, Vertex AI, Google ADK, Google AI Studio, and Google Workspace integrations.
• Proficiency in Python (primary) and familiarity with Node.js.
• Strong background in cloud-native development on GCP.
• Demonstrated experience with model fine-tuning (LoRA, QLoRA, PEFT) and model evaluation frameworks.
• Solid understanding of MLOps, CI/CD for AI systems, and production deployment best practices.
• Experience with multi-agent AI architectures using Semantic Kernel, or LangGraph.

 
Preferred Qualifications:

• Google Cloud Professional certifications ( 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 engineers or interns in AI/ML domains.

 

At SPECTRAFORCE, we are committed to maintaining a workplace that ensures fair compensation and wage transparency in adherence with all applicable state and local laws. This position's starting pay is: $97.00/hr.