Lead Applied Value Engineer

Celonis Celonis · Data AI · United Arab Emirates · Remote · Value Engineering

Lead Applied Value Engineer at Celonis, focusing on architecting and prototyping AI solutions, specifically LLM/agent systems with RAG and tool use, for enterprise customers. The role involves deep technical discovery, end-to-end PoV execution, and ensuring value realization, requiring strong skills in agentic AI, ML/LLMOps, and software engineering within a regulated enterprise context.

What you'd actually do

  1. Discovery & Advanced AI Solutioning: Act as the foremost Celonis product and domain positioning expert. Seamlessly translate deep domain expertise and complex customer requirements into flawless enterprise architecture. You will lead technical discovery to architect, prototype, and build bespoke, state-of-the-art AI solutions targeting nuanced operational pain points.
  2. AI Solution Development: Lead the end-to-end execution of high-stakes Proof-of-Value (PoV) projects. Architect and deliver highly secure, scalable LLM/agent systems with advanced RAG, tools, and strict guardrails; integrating flawlessly with rigid enterprise data, identity, and compliance frameworks.
  3. Value Selling & Realization: Act as a trusted advisor, maintaining active involvement with strategic accounts to ensure programmatic value realization until agreed-upon value, massive adoption thresholds, and operational transformations are achieved.
  4. Domain & Industry Specialization: Leverage deep domain expertise combined with cross-system, AI-driven solutioning to architect industry-specific E2E solutions. You will codify this work into reusable methodology and globally scalable assets to accelerate time-to-value and mentor junior peers.

Skills

Required

  • Executive-Facing Presales & Value Engineering (7+ years)
  • Domain & Industry Expertise
  • Agentic AI Systems
  • Math & Operations Research
  • Production ML/LLMOps at Scale
  • Software/Data Engineering
  • AI Architecture & Security
  • Strong presentation skills
  • Masters Degree in computer science, engineering, mathematics or related fields, or equivalent work experience

Nice to have

  • Python proficiency
  • modular API design
  • creating reusable libraries
  • advanced SQL
  • scalable data integration using ML/AI frameworks (PyTorch, TensorFlow, scikit-learn, XGBoost, Hugging Face)
  • AWS Bedrock, Azure AI, GCP Vertex AI

What the JD emphasized

  • state-of-the-art AI
  • state-of-the-art AI solutions
  • LLM/agent systems
  • advanced RAG
  • strict guardrails
  • Agentic AI Systems
  • LLM orchestration
  • tool use/function calling
  • RAG
  • agents
  • prompt engineering
  • agentic patterns
  • Production ML/LLMOps at Scale
  • AI Architecture & Security
  • secure enterprise cloud environments
  • stringent adherence to identity and access management (IAM) protocols
  • data governance frameworks
  • global privacy laws
  • overarching security standards

Other signals

  • architect and prototype state-of-the-art AI and technical solutions
  • architect and deliver highly secure, scalable LLM/agent systems with advanced RAG, tools, and strict guardrails
  • Advanced architectural mastery of LLM orchestration, tool use/function calling, RAG, agents, prompt engineering, and agentic patterns for enterprise workflows
  • Production ML/LLMOps at Scale