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Lead Software Engineer - GenAI / GCP

Robotics Technologies LLC
Minneapolis, MN, United States
4 days ago
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Role details

Contract type
Temporary contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Compensation
$141,440
Working hours
Regular working hours
Job source
www.indeed.com

Tech stack

LangGraph Framework Artificial Intelligence Amazon Web Services Software Applications Microsoft Azure Cloud Computing Data Mining Google Docs Python (Programming Language) Machine Learning Natural Language Processing Open Source Technology
+19 more
Performance Tuning OpenAI Search Technologies Software Deployment Pytorch Retrieval-Augmented Generation Transfer Learning Flask (Web Framework) Large Language Models Google Vertex AI Deep Learning Generative AI Agentic-AI Fastapi Containerization CrewAI Restful APIs Intelligent Document Processing Docker

Requirements

  • Strong hands-on experience in Python, Google Vertex AI, and Google Document AI (DocAI).
  • Design, develop, and implement enterprise-grade Generative AI and LLM-based solutions.
  • Build RAG-based applications using retrieval, embeddings, and vector databases.
  • Implement enterprise-level Agentic AI workflows using LangGraph, Google ADK, CrewAI, or similar frameworks.
  • Experience with LLM fine-tuning, deployment, optimization, and performance tuning.
  • Develop intelligent document processing solutions using OCR, Google Document AI, and automated data extraction.
  • Strong understanding of NLP, Machine Learning, Deep Learning, and PyTorch.
  • Experience working with OpenAI APIs and open-source Large Language Models (LLMs).
  • Hands-on experience with vector databases, embedding models, and semantic search.
  • Develop scalable AI applications using Python, FastAPI, Flask, and REST APIs.
  • Experience with Docker and containerized application deployment.
  • Strong cloud experience with GCP, AWS, or Azure.
  • Build and integrate AI-powered applications for enterprise business use cases.
  • Optimize AI solutions for scalability, reliability, performance, and production deployment

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