Senior Lead, AI Engineering, GDBS

South Pole is an energetic, global company offering comprehensive sustainability solutions and services. With offices spanning all continents across the globe, we strive to create a sustainable society and economy that positively impacts our climate, ecosystems and developing communities. With our solutions, we inspire and enable our customers to create value from sustainability-related activities.
Our Corporate Functions team is the “enablement engine” of South Pole. Behind the scenes, we provide the business with world-class solutions and best practices that sustain business growth and success. We cover key areas such as Finance, HR, Office Management, Legal, IT, Operations, etc. If you are a smart, ambitious and dynamic professional with a strong passion to make a real difference in the fight against climate change, the below position may be right for you
Job summary:
South Pole is looking for an AI Developer to help build the intelligent layer behind our products. You will design, build, and ship production-grade AI features: LLM-powered workflows, retrieval pipelines, and agentic tooling that turn large, messy environmental and carbon-market data into reliable answers.
This is a hands-on engineering role pitched at a medior-to-senior level. We are open to a strong medior developer ready to step up, as well as a senior who can take a vague requirement and turn it into a tested, well-reasoned feature. What matters is that you understand the why behind your choices - not just how to call an API, but when not to, and how to keep an AI system trustworthy, evaluated, and cost-aware in production.
Key Responsibilities:

• AI Feature Development: Design and ship LLM-powered features end to end - retrieval-augmented generation (RAG), structured extraction, summarisation, and agentic / tool-using workflows.
• Pipelines & Integration: Build robust data and inference pipelines that connect our AI layer to product back-ends, vector stores, and source systems (AQ, Salesforce, Google Workspace).
• Evaluation & Reliability: Stand up evaluation, monitoring, and guardrails so AI features are measurably accurate, safe, and stable - not just impressive in a demo.
• Performance & Cost: Optimise latency, token usage, and throughput; apply caching, batching, and model-routing to keep large workloads fast and economical.
• Quality Assurance: Champion rigorous testing and pull-request discipline to prevent regressions and keep deployments stable.
• Collaboration: Partner with the Tech Lead and product owners to translate business problems into AI solutions, and flag where a non-AI approach is the better call.
  

• LLM Engineering: Hands-on experience building with LLM APIs (e.g. Anthropic, OpenAI, or Vertex AI / Gemini) - prompt design, function/tool calling, and structured outputs.
• RAG & Retrieval: Practical knowledge of embeddings, chunking, and vector search (pgvector, or a managed vector DB).
• Frameworks: Familiarity with at least one orchestration framework (LangChain, LlamaIndex, or equivalent), and comfort working without one when it’s simpler.
• Evaluation: Awareness of LLM evaluation, hallucination mitigation, and basic red-teaming of AI outputs.
  

• Back-end: Strong Python (FastAPI / Flask / Django), with clean, testable, production-oriented code.
• Database: Solid PostgreSQL - comfortable with relations, indices, constraints, and transactions beyond basic ORM usage.
• Data: Experience handling large, high-volume datasets and streaming / batch processing.
  

• Cloud: Experience with GCP (Vertex AI, Cloud Run, Pub/Sub) or a comparable cloud platform.
• Event-Driven Design: Proficiency with queues and Pub/Sub for asynchronous, event-driven workflows.
• DevOps Fundamentals: Working knowledge of CI/CD workflows and containerisation (Docker; Kubernetes a plus).
• Security: Sound practices around authentication, authorisation, throttling, and handling of sensitive and proprietary data in AI workflows.Systems & Cloud-Native Architecture
• Cloud: Experience with GCP (Vertex AI, Cloud Run, Pub/Sub) or a comparable cloud platform.
• Event-Driven Design: Proficiency with queues and Pub/Sub for asynchronous, event-driven workflows.
• DevOps Fundamentals: Working knowledge of CI/CD workflows and containerisation (Docker; Kubernetes a plus).
• Security: Sound practices around authentication, authorisation, throttling, and handling of sensitive and proprietary data in AI workflows.
  

• Proficiency in business English.
• Ability to translate complex technical and AI concepts into understandable terms for non-technical business colleague