Applied Value Engineer

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

Senior Applied Value Engineer at Celonis, a leader in Process Intelligence, focusing on building and implementing AI solutions for enterprise customers. The role involves understanding customer AI strategies, prototyping solutions using cutting-edge AI technologies, and enabling agentic process transformation. Key responsibilities include end-to-end execution of Proof-of-Value projects, integrating LLM/agent systems with RAG, tools, and guardrails, and ensuring value realization. Requires experience in technical pre-sales, AI roadmaps, prototyping generative AI solutions, and understanding of Python and ML libraries.

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: Senior Applied Value Engineers stay involved with projects until agreed value & adoption thresholds are reached

Skills

Required

  • 6+ years of experience leading technical pre-sales, including defining AI roadmaps, building compelling ROI/TCO business cases and prototyping of machine learning and generative AI solutions.
  • Understanding of generative AI techniques like RAG, few shot learning, prompt engineering, multi-agent orchestration, multimodal understanding, or fine-tuning that are used to build high-impact use cases like intelligent chatbots and automated text processors.
  • Understanding of business processes across sectors (such as Supply Chain or Finance) with the 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) as well as data engineering tools and technologies.
  • Strong presentation skills to both internal and external stakeholders (including executives), whether whiteboarding sessions or formal readouts and demos.
  • Bachelor’s Degree required

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)
  • Masters Degree in computer science, engineering, mathematics or related fields, or equivalent work experience preferred.

What the JD emphasized

  • building Celonis solutions using the world’s leading Process Intelligence (PI) platform in combination with the largest AI and ML technology partners, such as Microsoft, OpenAI and Databricks
  • industrialize AI unlocking real ROI on AI deployments and at scale
  • translate customer requirements into innovative solutions
  • rapidly build creative prototypes in customer hackathons
  • achieving real ROI out of AI deployments at scale
  • use of autonomous AI agents
  • deliver secure, scalable LLM/agent systems with RAG, tools, and guardrails
  • integrating with enterprise data, identity, and compliance frameworks
  • Hands-on experience building agentic systems using LLM orchestration, RAG, function calling, and prompt engineering, while ensuring safety through rigorous evaluations and guardrails

Other signals

  • industrialize AI unlocking real ROI on AI deployments and at scale
  • translate customer requirements into innovative solutions
  • rapidly build creative prototypes in customer hackathons
  • 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
  • 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