Generative AI Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Generative AI Engineer based in Mountain View, CA, for 12 months, offering a competitive pay rate. Key skills include Python, LangChain, cloud LLM providers, and workflow orchestration. Experience in RAG pipelines and prompt engineering is essential.
🌎 - Country
United States
💱 - Currency
$ USD
-
💰 - Day rate
560
-
🗓️ - Date discovered
September 24, 2025
🕒 - Project duration
More than 6 months
-
🏝️ - Location type
On-site
-
📄 - Contract type
Unknown
-
🔒 - Security clearance
Unknown
-
📍 - Location detailed
Mountain View, CA
-
🧠 - Skills detailed
#Security #Observability #Airflow #Azure #AI (Artificial Intelligence) #Langchain #Monitoring #Python #AWS (Amazon Web Services) #Cloud
Role description
GenAI Engineer
Location: Mountain View , CA
Duration: 12 months
The project is focused on building production-grade GenAI solutions with emphasis on:
• RAG pipelines leveraging LLMs
• Prompt engineering (system/tool prompts, function calling, versioning with evals)
• Evaluation & observability (ground truth setup, confusion metrics, LLM-as-judge with human review, cost & latency monitoring)
• Retrieval strategies & prompt patterns (context management, hallucination mitigation)
• LangChain/LlamaIndex (or equivalent) proficiency
• Cloud LLM providers (Azure OpenAI, AWS Bedrock, Vertex AI)
• Workflow orchestration (Airflow, Dagster)
• Security & privacy (PII handling, RBAC)
• Python expertise with strong practical experience
Success criteria:
• A production-ready prompt + RAG pipeline system with measurable quality lift (accuracy/confusion metrics) and reduced hallucinations, backed by a repeatable evaluation harness.
GenAI Engineer
Location: Mountain View , CA
Duration: 12 months
The project is focused on building production-grade GenAI solutions with emphasis on:
• RAG pipelines leveraging LLMs
• Prompt engineering (system/tool prompts, function calling, versioning with evals)
• Evaluation & observability (ground truth setup, confusion metrics, LLM-as-judge with human review, cost & latency monitoring)
• Retrieval strategies & prompt patterns (context management, hallucination mitigation)
• LangChain/LlamaIndex (or equivalent) proficiency
• Cloud LLM providers (Azure OpenAI, AWS Bedrock, Vertex AI)
• Workflow orchestration (Airflow, Dagster)
• Security & privacy (PII handling, RBAC)
• Python expertise with strong practical experience
Success criteria:
• A production-ready prompt + RAG pipeline system with measurable quality lift (accuracy/confusion metrics) and reduced hallucinations, backed by a repeatable evaluation harness.
