Senior Applied Value Engineer - Consumer Products and Retail

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

This role focuses on applying AI and ML technologies, specifically generative AI and agentic systems, to solve business-critical problems for enterprise customers using Celonis' Process Intelligence platform. The Senior Applied Value Engineer will prototype, demonstrate value, and ensure successful implementation of these solutions, with a strong emphasis on achieving ROI through AI deployments at scale and enabling autonomous AI agents.

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 Engineer 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
  • industrialize AI unlocking real ROI on AI deployments and at scale
  • enabling a fundamental shift in business operations from traditional, rule-based automation to the use of autonomous AI agents
  • deliver secure, scalable LLM/agent systems with RAG, tools, and guardrails
  • 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.
  • 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
  • enabling a fundamental shift in business operations from traditional, rule-based automation to the use of autonomous AI agents