Company Description
Amplytic is building an AI asset manager for renewable energy portfolios across wind, solar, and storage. It creates a structured understanding of each asset by linking operational data, commercial contracts, and regulatory rules through an ontology-led intelligence layer. This gives owners and managers clear, reliable answers on obligations, performance, risks, and required actions.
Amplytic also automates the routine work that slows asset management down: tracking contract duties, identifying compliance items, checking revenue logic, validating settlements, and interpreting regulatory rules. These become consistent, traceable outputs rather than manual spreadsheets and scattered documents.
By reducing operational drag and improving the quality of decisions, Amplytic helps teams run larger portfolios with less overhead and achieve stronger investment yield across their assets.
Role Description
We are hiring an AI Engineer to work on the core intelligence systems at Amplytic. This is a full-time role. You will design and build agentic components that process renewable-energy data, classify contracts, extract structure from documents, and update our knowledge graph in real time. The work spans NLP, retrieval, agentic RAG across large document sets, ontology construction, and deploying AI agents into live production environments. A solid understanding of modern AI-agent frameworks and how to use them in real systems is important.
You will build pipelines that continuously ingest information, run document classification and entity extraction, and feed structured updates into our renewable-energy ontology. You will also work on agentic decision systems that trigger alerts, recommendations, and automated actions using real-time data and retrieval-augmented context. The role requires comfort with ML systems, RAG architectures, natural-language interfaces, and the practical side of shipping AI features that solve real business problems.
Qualifications
• Proven experience in software engineering, with strong proficiency in Python.
• Solid understanding of core ML fundamentals: supervised/unsupervised learning, model evaluation, and feature engineering.
• Experience working with LLM frameworks such as LangChain, LlamaIndex, or similar agent-based AI frameworks.
• Understanding of agent architecture: tool use, multi-agent patterns, and autonomous decision-making workflows.
• Experience building or working with RAG systems, ideally in domain-specific or document-heavy environments.
• Hands-on exposure to document classification, entity extraction, or large-scale information retrieval.
• Experience with knowledge graphs or ontology-driven systems: entity resolution, relation extraction, or schema design.
• Comfort deploying ML models and AI agents into production, including monitoring, retraining, and pipeline maintenance.
• Hands-on experience with cloud platforms such as AWS, Azure, or Google Cloud.
• Strong problem-solving skills and the ability to work through messy, real-world data.
• Curiosity about the renewable energy space or financial/contract logic is an asset.
• Experience in a fast-paced startup environment is a plus.
What we value and how we work
We operate with high ownership and zero excuses. We care about solving real customer problems. We move fast, make bold calls, and favour progress over polish. Clear thinking and relentless execution matter most.
Everyone here is a builder. You get space to act, challenge ideas, call out inefficiency, and push for better ways of doing things. We keep teams small and trust people closest to the problem to make decisions.
In this role, you will get
Real ownership: You will not be a background ML helper; you will own core AI systems end to end. You will build the intelligence layer behind an AI asset manager: agentic workflows, retrieval pipelines, knowledge-graph updates, and the ML components that drive real decisions.
A decisive environment: No layers and no vague asks. You get context, autonomy, and the mandate to move. You will design and ship agentic features end-to-end — from prompt logic to RAG pipelines to production deployment.
High trust, low bureaucracy: Small teams, direct communication, and freedom to build without needless gates. If something can be improved — data flows, model performance, retrieval quality — you fix it.
A mission that matters: You will work on production AI systems that manage millions in clean-energy assets. Your work will support better financial outcomes for renewable projects and help keep the energy transition sustainable.
Deep technical growth: You will work across real-time data, document intelligence, ontology-driven modelling, agent frameworks, retrieval systems, and full-stack engineering. Expect steep learning curves and complex, ambiguous problems.
Meaningful upside: Competitive salary plus meaningful equity, with direct influence on the company’s trajectory.