Senior Applied Value Engineer

Key Responsibilities

AI Discovery & Solutioning: Understand customers' AI strategy and sector-specific challenges (e.g., predictive maintenance, outage management, supply chain resilience, Quality Management). Find the best problem-solution fit and translate customer requirements into innovative solutions.

Pre- and Post-Sales Execution: Actively drive the full customer lifecycle. Lead technical discovery and capability demonstrations during the pre-sales cycle, and remain deeply involved post-sale to guide implementation, ensuring agreed value and adoption thresholds are successfully reached.

Hackathons & Prototyping: Leverage cutting-edge AI technologies to rapidly build creative prototypes in customer hackathons, solving critical pain points specific to Supply Chain, Manufacturing, Grid Operations and Warranty and Quality.

Agentic Process Transformation: Support customers in achieving real ROI out of AI deployments at scale, enabling a fundamental shift from traditional, rule-based automation to the use of autonomous AI agents empowered by the Celonis Process Intelligence Platform.

Proof Projects: End-to-end execution of business-critical Proof-of-Value projects. This includes architecting and delivering secure, scalable LLM/agent systems with RAG, tools, and guardrails, while seamlessly integrating with enterprise data, identity protocols, and stringent energy compliance frameworks.

Domain & Industry Leadership: Serve as the internal and external technical subject matter expert for the Energy vertical, scaling knowledge across the organization regarding utilities, upstream/downstream oil & gas, and renewable energy processes.

Requirements

• 4+ years of experience leading end-to-end technical pre-sales and post-sales engagements specifically within manufacturing and production. This includes defining AI roadmaps, building compelling ROI/TCO business cases, and guiding technical implementations through to value realization.
• Deep understanding of business processes native and industry knowledge in Energy and/or Manufacturing & Production with in-depth experience in domains such as Asset Management, Field Services, Supply Chain, Capital Projects, or Trading.
• Solid knowledge of Python and common ML libraries (such as LangChain, pandas, pydantic, sklearn, PyTorch) as well as data engineering tools and technologies relevant to handling large-scale industrial data.
• Strong presentation skills to both internal and external stakeholders (including C-level executives and operational leaders), whether leading technical whiteboarding sessions or formal readouts and demos.
• Bachelor's Degree required; Master's Degree in computer science, engineering, mathematics, or related fields, or equivalent work experience preferred.

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 suited for highly regulated industries.
• 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) and familiarity with IT/OT convergence and industrial IoT data structures.
• Expertise in generative AI techniques like RAG, few-shot learning, prompt engineering, multi-agent orchestration, multimodal understanding, or fine-tuning used to build high-impact use cases.