Generative AI Engineer – (Google Cloud Platform Platform Preferred)
Location: Remote. (Consultant can be located anywhere in the US, but must work EST.)
Work Authorization: USC
Core Responsibilities
Model Fine-Tuning & Customization: Adapting base foundation models (like Gemini, GPT-4, or Llama) to domain-specific datasets using techniques like LoRA (Low-Rank Adaptation) and RLHF (Reinforcement Learning from Human Feedback).
Building Orchestration & RAG Pipelines: Designing Retrieval-Augmented Generation (RAG) pipelines to connect LLMs with external enterprise databases, APIs, and real-time enterprise data.
Agentic Workflows: Developing autonomous, multi-step AI agents using frameworks like LangGraph, CrewAI, or the Model Context Protocol (MCP) to let models execute tasks, call APIs, and self-correct.
Prompt Engineering & System Guards: Crafting robust system prompts, evaluation frameworks, and guardrails (e.g., preventing hallucinations, prompt injections, or sensitive data leaks).
Production Deployment (LLMOps/MLOps): Containerizing models and application code, optimizing latency, and scaling AI services on cloud platforms like Google Cloud Platform (Google Cloud Platform), AWS, or Azure.
Required Skills
Languages: Python (primary), TypeScript / JavaScript
Frameworks: LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, PyTorch, Hugging Face
Vector DBs: Pinecone, Chroma, Qdrant, Vertex AI Vector Search, Weaviate
Foundation Models: Gemini, Claude, GPT, DeepSeek, open-source models (Llama, Mistral)
Cloud & Ops: Google Cloud Platform (Vertex AI, Cloud Run), AWS (Bedrock, SageMaker), Docker, Kubernetes
Required Qualifications
Bachelor’s degree in computer science, Engineering, or a related field, or equivalent practical experience.
8+ years in software or infrastructure engineering, including 3+ years as a AI engineer.
Demonstrated production experience designing, building, and deploying agents on Gemini, Claude, or GPT — including tool/function calling, orchestration, and state management. Prototypes and demos do not qualify; we are looking for agents that survive real users.
Strong grasp of cloud security fundamentals: identity, network segmentation, encryption, and secrets management.
Preferred Qualifications
Hands-on experience with Vertex AI Agent Builder, Agent Development Kit, or Agent Engine, and with deploying against Gemini Enterprise.
Experience with agent frameworks and protocols such as LangGraph, MCP (Model Context Protocol), or equivalent tool-integration standards.
Experience evaluating agent quality systematically — task benchmarks, regression suites, and failure analysis — rather than by manual spot-checking.
Practical experience with Claude Code and authoring Claude Skills in a team setting.
Experience with Vertex AI beyond inference: pipelines, model registry, feature store, and evaluation tooling.
Data platform experience with BigQuery, Dataflow, Dataform, or Composer.