Integration Engineer II - AI and Python Developer

This role focuses on building, testing, and shipping software as part of an agile engineering team, with a strong emphasis on AI and Generative AI solutions. The Integration Engineer II will work hands-on with AI/ML, Generative AI, and agentic AI, developing and prototyping use cases, evaluating foundation models, and contributing to RAG, memory systems, tool use, and multi-agent workflows. The role also involves supporting MLOps, AI Ops, and promoting responsible AI practices, with a requirement for Python and ReactJS experience, and experience in AI/ML fundamentals, LLMs, and cloud AI platforms.

What you'd actually do

  1. Work hands-on with AI/ML, Generative AI, and agentic AI solutions.
  2. Help develop and prototype AI use cases that solve real business problems.
  3. Evaluate and apply foundation models, LLMs, and modern AI frameworks.
  4. Contribute to RAG, memory systems, tool use, and function-calling patterns.
  5. Support multi-agent workflows and autonomous AI capabilities.

Skills

Required

  • Python
  • ReactJS
  • full system lifecycle development
  • AI/ML fundamentals
  • model evaluation
  • LLMs
  • prompt engineering
  • fine-tuning
  • multimodel AI
  • client-facing development
  • agentic frameworks (LangChain, Strands, or Crew AI)
  • vector databases
  • RAG
  • tool/function calling
  • Cloud AI platforms (AWS Bedrock, Azure, or GCP)
  • MLOps
  • CI/CD for AI solutions
  • communication skills
  • teamwork

Nice to have

  • global Agile teams
  • cross-functional teams
  • CI/CD pipelines
  • DevOps practices
  • mentoring
  • cloud certifications

What the JD emphasized

  • hands-on experience in Python and ReactJS
  • 1+ years’ experience in AI/ML, Generative AI, or advanced analytics
  • 2+ years of experience in ML fundamentals and model evaluation
  • 1+ years’ experience in LLMs, prompt engineering, fine-tuning, and multimodel AI
  • 1+ years’ experience with vector databases, RAG, and tool/function calling
  • Strong understanding of MLOps and CI/CD for AI solutions

Other signals

  • AI/ML, Generative AI, and agentic AI solutions
  • foundation models, LLMs, and modern AI frameworks
  • RAG, memory systems, tool use, and function-calling patterns
  • multi-agent workflows and autonomous AI capabilities