Applied Value Engineer

Celonis Celonis · Data AI · Bangalore, India · Value Engineering

Celonis is seeking an Applied Value Engineer to work with strategic customers, understanding their AI strategies and business challenges. The role involves building Celonis solutions using their Process Intelligence platform combined with AI/ML partners like OpenAI. Responsibilities include AI discovery, solutioning, prototyping creative solutions in hackathons, enabling agentic process transformation, executing proof-of-value projects with LLM/agent systems (RAG, tools, guardrails), and ensuring successful project outcomes and adoption. The role requires experience in technical pre-sales, AI roadmaps, prototyping ML/generative AI solutions, understanding generative AI techniques (RAG, multi-agent orchestration, multimodal, fine-tuning), Python, ML libraries, and business processes. Experience in the Oil & Gas industry is a must.

What you'd actually do

  1. AI Discovery & Solutioning: Understand customers AI strategy and business critical challenges. As Celonis product & domain expert, find the best problem-solution fit and translate customer requirements into innovative solutions that move the needle
  2. Hackathons & Prototyping: Think out of the box, have a „can-do“ attitude and don’t shy away from complex problems. Leverage cutting edge AI technologies to rapidly build creative prototypes in customer hackathons solving business critical problems
  3. Agentic Process Transformation: Support our customers in achieving real ROI out of AI deployments at scale enabling a fundamental shift in business operations from traditional, rule-based automation to the use of autonomous AI agents empowered by our Celonis Process Intelligence Platform
  4. Proof projects: End-to-end execution of business-critical Proof-of-Value projects, incl. architecture and deliver secure, scalable LLM/agent systems with RAG, tools, and guardrails; integrating with enterprise data, identity, and compliance frameworks.
  5. Ensure Successful Project Outcome: Applied AI Engineers stay involved with projects until agreed value & adoption thresholds are reached

Skills

Required

  • 5+ years of experience leading technical pre-sales
  • defining AI roadmaps
  • building compelling ROI/TCO business cases
  • prototyping of machine learning and generative AI solutions
  • Experience in Oil & Gas industry
  • Understanding of generative AI techniques like RAG, few shot learning, prompt engineering, multi-agent orchestration, multimodal understanding, or fine-tuning
  • Understanding of business processes across sectors (such as Supply Chain or Finance)
  • ability to translate high-level business needs into specific AI use cases
  • Good knowledge of Python and common ML libraries (such as LangChain, pandas, pydantic, sklearn, PyTorch)
  • data engineering tools and technologies
  • Strong presentation skills to both internal and external stakeholders (including executives)

Nice to have

  • Hands-on experience building agentic systems using LLM orchestration, RAG, function calling, and prompt engineering, while ensuring safety through rigorous evaluations and guardrails.
  • Working knowledge of tools in the LLM ecosystem such as LangChain, LlamaIndex, or other OSS packages.
  • Experience in deploying and monitoring models at scale across major cloud platforms (AWS Bedrock, Azure AI, GCP Vertex)

What the JD emphasized

  • Experience in Oil & Gas industry is must.
  • customer requirements
  • business critical problems
  • AI strategy
  • AI deployments at scale
  • business-critical Proof-of-Value projects
  • secure, scalable LLM/agent systems

Other signals

  • customer-facing
  • prototyping
  • LLM/agent systems
  • RAG
  • tools
  • guardrails
  • enterprise data integration
  • AI strategy
  • ROI