Senior Applied Value Engineer - UKI Energy, Oil & Gas
Leads technical pre-sales and develops AI-powered Celonis solutions for strategic customers, from discovery and prototyping through Proof-of-Value delivery and adoption. The role requires extensive AI pre-sales experience, generative AI expertise, Python and ML skills, and executive-level communication.
About the job
Responsibilities
- Understand customers’ AI strategies and business-critical challenges; translate requirements into innovative Celonis solutions.
- Lead customer hackathons and rapidly prototype solutions using AI technologies.
- Enable agentic process transformation and help customers achieve measurable ROI from AI deployments at scale.
- Execute business-critical Proof-of-Value projects end to end, including architecture and delivery of secure, scalable LLM and agent systems with RAG, tools, and guardrails.
- Integrate solutions with enterprise data, identity, and compliance frameworks.
- Remain involved through agreed value and adoption thresholds to ensure successful outcomes.
- Develop specialization in relevant business domains or industries and scale knowledge across the organization.
Requirements
- 6+ years of experience leading technical pre-sales, including AI roadmaps, ROI/TCO business cases, and machine learning or generative AI prototypes.
- Understanding of generative AI techniques including 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, with the ability to translate business needs into AI use cases.
- Good knowledge of Python and common machine learning libraries and data engineering technologies.
- Strong presentation skills for internal and external stakeholders, including executives.
- Bachelor’s degree required; a master’s degree in computer science, engineering, mathematics, or a related field, or equivalent experience, preferred.
Nice to Have
- Hands-on experience building agentic systems using LLM orchestration, RAG, function calling, and prompt engineering, with rigorous evaluations and guardrails.
- Experience with LangChain, LlamaIndex, or other open-source LLM packages.
- Experience deploying and monitoring models at scale on AWS Bedrock, Azure AI, or Google Cloud Vertex AI.
Compensation and Benefits
- Restricted Stock Units and merit-based refresh grants for full-time employees.
- Paid parental leave, including 24 weeks for primary carers and 12 weeks for supporting carers.
- Flexible hybrid work model, with PTO policies varying by region.
- Learning framework, mentorship programs, and access to a dedicated learning platform.
- Subsidized Wellhub memberships, mental health counseling, and wellness programs.
- Paid annual volunteer time through Impact Days.
Skills
Python, RAG, Prompt Engineering, LangChain, pandas, Pydantic, scikit-learn, PyTorch, Llamaindex, Llm Orchestration, Function Calling, Aws Bedrock, Azure Ai, Google Cloud Vertex Ai, Multi-Agent Systems
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