Job Title: Senior AI Engineer – Google AI & Generative Intelligence
Duration: 6 Months (Temp-to-Hire)
Location: Paramus, NJ [Hybrid]

About Spectraforce:
Established in 2004, Spectraforce is one of the largest and fastest-growing diversity-owned staffing firms in the US. Our global client service delivery model is powered by a proprietary AI talent acquisition platform, robust ISO-certified processes, and passionate, client-engaged teams. We provide talent and project-based solutions to over 140 clients across multiple industries, including Technology, Financial Services, Life Sciences, Healthcare, Telecom, Retail, Utilities, and Transportation. Spectraforce is built on the concept of “human connection,” defined by our NEWJOBPHORIA® branding—bringing joy and freedom to the work lifestyle so our people and clients can reach their highest potential. Learn more at: www.spectraforce.com

Benefits:
Spectraforce offers ACA-compliant health benefits, dental, vision, accident, critical illness, voluntary life, and hospital indemnity insurances to eligible employees. Additional benefits include commuter benefits, a 401K plan with matching, and a referral bonus program. Paid sick leave and unpaid leave are provided as required by law.

Equal Opportunity Employer:
Spectraforce is an equal opportunity employer and does not discriminate against any employee or applicant for employment because of race, religion, color, sex, national origin, age, sexual orientation, gender identity, genetic information, disability, veteran status, or any other protected category.

Role Overview

We are seeking a highly experienced Senior AI Engineer with deep expertise in Google AI technologies and 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, and VSCode for AI application development and prototyping.
• Utilize Google Cloud Platform (GCP) services including Vertex AI, GKE, Cloud Run, Cloud Functions, and Vertex AI Vector DBs.

3. Design & Planning

• Lead requirements gathering using Confluence.
• Create system architecture diagrams and AI workflows using Lucidchart.
• Design UI/UX prototypes in Figma.
• Manage project delivery and sprint planning using Jira.
• Oversee data preparation and management for AI/ML workflows.
• Conduct data analysis using Jupyter Notebooks and pandas.
• Leverage Hugging Face Model Hub for model comparison and selection.

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.
• Track experiments with MLflow or Weights & Biases.
• Manage code and data versioning with Git.

5. Vector Databases & Semantic Search

• Implement semantic search and RAG pipelines using Vertex AI Vector DBs and ChromaDB.
• Design and optimize end-to-end RAG architectures.

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 UI components.
• Prototype UI/UX workflows using Figma.

8. Development Tools & Code Quality

• Write and debug code in VS Code with Python and GitHub Copilot extensions.
• Manage source code with GitHub or GitLab.
• Enforce code quality using SonarQube, ESLint, and Pylint.

9. Testing & Quality Assurance

• Conduct LLM-specific testing using RAGAS and DeepEval.
• Use LangSmith Evaluators for prompt testing and hallucination detection.
• Write and execute unit tests using pytest.
• Ensure output quality 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.

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 for model quality and reliability.

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.
• 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.

Spectraforce is committed to diversity, inclusion, and equal opportunity. We encourage candidates from all backgrounds to apply.