Description

Key Responsibilities

• Design and build GenAI solutions, including:
  • Agent-based architectures
  • RAG (Retrieval-Augmented Generation) pipelines
  • AI-powered tools and automation workflows
• Develop and maintain agent frameworks, including integration with MCPs (Model Context Protocols / multi-component systems)
• Take proofs of concept (POCs) and:
  • Mature them into scalable solutions
  • Drive adoption across teams
  • Bring them into production environments
• Partner across teams to $B!H(Bmarket$B!I(B and evangelize AI solutions internally
• Work hands-on across the stack, including coding, architecture, and infrastructure design
• Operate in a high data volume environment, requiring strong data engineering fundamentals

Required Technical Skillset

• GenAI / LLM Engineering (core requirement):
  • Experience building agents and AI workflows
  • Hands-on with RAG pipelines
  • Strong understanding of LLM integration and orchestration
• Programming & Data:
  • Python (primary language for AI/agent development)
  • SQL (data access, transformation, querying large datasets)
  • Experience working with large-scale data environments
• Cloud & Infrastructure:
  • Strong experience with AWS
  • Understanding of deploying and scaling AI solutions in cloud environments
• Engineering Mindset:
  • Hands-on coder (not purely conceptual/architectural)
  • Ability to build from scratch and iterate quickly

What $B!H(BGood$B!I(B Looks Like

• Deep, hands-on GenAI builder (not just theoretical AI knowledge)
• Proven ability to:
  • Build working agents and AI tools
  • Take ideas from concept $B"*(B POC $B"*(B production
• Comfortable operating in ambiguous, fast-moving environments
• Strong communicator who can drive adoption across business and tech teams

Key Skills

Education

Any Gradute