Senior Applied Value Engineer - Uki Oil & Gas

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

This role focuses on applying AI and ML technologies, particularly generative AI and autonomous agents, within the Celonis Process Intelligence platform to solve business-critical problems for enterprise customers in the Oil & Gas industry. The engineer will be responsible for understanding customer needs, prototyping solutions, building and delivering LLM/agent systems with RAG and tools, and ensuring value realization and adoption. The role involves technical pre-sales, solutioning, and hands-on prototyping with a strong emphasis on delivering ROI through AI deployments at scale.

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, including defining AI roadmaps, building compelling ROI/TCO business cases and 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)
  • 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.
  • 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

  • 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
  • prototype these solutions, demonstrate their value to Executives and ensure successful implementation, adoption and value realization
  • 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
  • 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.