Senior Lead, AI Engineering, GDBS
Pubblicato il 27-10-2026 - SOUTH POLE in Milano
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, this 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.
Technical Requirements - Core Stack
AI & Machine Learning
- 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 & Data
- 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.
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.
Communication Skills
- Proficiency in business English.
- Ability to translate complex technical and AI concepts into understandable terms for non‑technical business colleagues.
Desirable Requirements
- Experience with MLOps / model lifecycle tooling, or fine‑tuning.
- Exposure to front‑end work (React / Next.js) for AI‑facing interfaces.
- Interest in climate, carbon markets, or sustainability data.
The Ideal Candidate Profile
- Proactive Communicator: You are proactive and communicative. In interviews you prefer a conversational approach – walking us through your reasoning and the trade‑offs