Senior Applied Value Engineer

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

This role focuses on applying AI technologies, specifically generative AI and LLMs, to solve mission-critical business problems for enterprise customers. The Senior Applied AI Value Engineer will work with customers to understand their challenges, prototype AI solutions using tools like OpenAI and Microsoft Azure, and ensure successful implementation and value realization. Key responsibilities include AI discovery, solutioning, prototyping, building agentic systems with RAG and tools, and integrating with enterprise data and compliance frameworks.

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: 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

  • 4+ 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 cutting edge AI technologies
  • industrialize AI unlocking real ROI on AI deployments and at scale
  • autonomous AI agents
  • secure, scalable LLM/agent systems with RAG, tools, and guardrails
  • integrating with enterprise data, identity, and compliance frameworks
  • Understanding of generative AI techniques like RAG, few shot learning, prompt engineering, multi-agent orchestration, multimodal understanding, or fine-tuning
  • 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

  • customer-facing AI solutions
  • AI agents
  • RAG
  • LLM integration
  • enterprise data integration