Job Description
The Applied AI Engineer in the Energy Sector is responsible for developing and implementing AI-driven solutions for strategic energy clients, focusing on process transformation, proof-of-value projects, and AI prototyping to optimize operations and unlock ROI across utilities, oil & gas, and renewable energy industries.
Key Responsibilities
- Understand energy customers' AI strategies and sector-specific challenges to develop tailored solutions.
- Lead technical discovery, capability demonstrations, and guide implementation during the pre-sales and post-sales cycles.
- Rapidly prototype AI solutions using cutting-edge technologies to address operational pain points in energy sectors.
- Support customers in achieving ROI through AI deployments, enabling autonomous AI agents and process transformation.
- Execute end-to-end Proof-of-Value projects, integrating AI systems with enterprise data and compliance frameworks.
- Serve as a technical subject matter expert for the Energy vertical, sharing knowledge internally and externally.
Requirements
- Minimum of 5 years of experience leading technical pre-sales and post-sales engagements specifically within the Energy sector, including Utilities, Oil & Gas, and Renewables.
- Deep understanding of business processes native to the Energy sector such as Asset Management, Field Services, Supply Chain, Capital Projects, or Trading, with the ability to translate high-level business needs into specific, impactful AI use cases.
- 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 such as automated processing of engineering documents, regulatory filings, or intelligent diagnostic chatbots.
- 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 in computer science, engineering, mathematics, or related fields, or equivalent work experience.
- Experience in deploying and monitoring models at scale across major cloud platforms such as AWS Bedrock, Azure AI, or GCP Vertex.
- Working knowledge of tools in the LLM ecosystem such as LangChain, LlamaIndex, or other open-source packages.
- Ability to architect and deliver secure, scalable LLM agent systems with RAG, tools, and guardrails, while seamlessly integrating with enterprise data, identity protocols, and energy compliance frameworks.
- Experience supporting the full customer lifecycle, including technical discovery, capability demonstrations during pre-sales, guiding implementation post-sale, and ensuring value realization.
- Ability to find the best problem-solution fit as a Celonis product and energy domain expert, translating customer requirements into innovative solutions.
- Experience in building agentic systems using LLM orchestration, RAG, function calling, and prompt engineering, with a focus on safety through rigorous evaluations and guardrails for highly regulated industries.
Benefits & Perks
generous PTO
hybrid working options
company equity RSUs
comprehensive benefits
extensive parental leave
dedicated volunteer days
access to gym subsidies
counseling and well-being programs
internal mobility and mentorship opportunities
Ready to Apply?
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