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.
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