Artificial Intelligence Engineer

Sigma Suisse; Genf; Vor Ort; CHF 140.000 - 180.000; Vollzeit

Zusammenfassung

Sigma Suisse is seeking an Artificial Intelligence Engineer in Geneva to join the AI platform team. You will design and build production-ready generative AI solutions, covering LLM platforms, agentic workflows and enterprise knowledge systems.

The role focuses on hands-on engineering, with responsibilities spanning architecture, implementation and safeguards to ensure robust, scalable AI services for internal and external teams. Fluency in English is required.

Qualifikationen

• Strong software engineering, production-ready delivery required.
• Experience building LLM-based applications or AI platforms in production.
• Proficiency with Python API and asynchronous services.
• Familiarity with major foundation models and cloud vendors.
• Hands-on with agent frameworks and retrieval systems.

Aufgaben

• Design and build AI applications and platforms for production.
• Create reusable components, APIs and libraries for teams.
• Define architectures, standards and patterns for GenAI applications.
• Implement safeguards for data, prompts and tool usage.
• Evaluate model performance, observability and regression tests.

Kenntnisse

Python API

Async frameworks

LLM platforms

Vector databases

Cloud deployment

Production systems

OpenAI API

Ausbildung

Degree in computer science or AI

Tools

LangGraph

LangChain

LlamaIndex

CrewAI

Jobbeschreibung

Sigma Suisse, based in Plan-les-Ouates, is a recruitment firm specialised in IT and strategic profiles, supporting influential players across French-speaking Switzerland.

Within our IT department, we work on mandates in Commodity and Financial Trading, Pharmaceutical, Aeronautics, Space, Energy, Watchmaking and Swiss foundations.

Artificial Intelligence Engineer

On behalf of our client, a leading international group, we are looking for a Artificial Intelligence Engineer to join their AI platform team in Geneva.

You will design and build the applications, platforms and shared capabilities that allow engineering teams to ship reliable, production ready generative AI solutions. The scope covers LLM platforms, agentic workflows, enterprise knowledge systems and reusable AI services supporting data retrieval, analysis and decision making. This is a hands on engineering role, not an advisory one.

Key responsibilities

• Build RAG services end to end, ingestion, parsing, chunking, indexing, retrieval, reranking, context assembly
• Develop shared platform capabilities for LLM applications, agentic systems and enterprise knowledge solutions
• Define reference architectures, engineering standards and implementation patterns for GenAI applications
• Set interoperability standards across models, tools and agents, including MCP and A2A
• Design agentic workflows interacting safely with internal APIs, databases, search systems and third party services
• Define standards for tool contracts, structured outputs, context management, prompting, model selection and routing
• Create reusable components, APIs and libraries for product and forward deployed engineering teams
• Evaluate LLM, retrieval and agent performance through automated metrics, test datasets, regression testing and human review
• Design safeguards against prompt injection, data leakage, unsafe tool execution and unreliable model behaviour
• Arbitrate technical trade offs across model quality, latency, cost, observability and resilience

Required experience

• Strong software engineering fundamentals, production systems designed, tested, deployed and operated
• Proven delivery of LLM based applications or AI platforms in production, not proof of concept only
• Python API and service development, ideally with modern asynchronous frameworks
• Foundation model APIs and platforms, OpenAI, Anthropic, Google Vertex AI or AWS Bedrock
• Agent frameworks and orchestration tools, LangGraph, LangChain, LlamaIndex, CrewAI or equivalent
• Vector databases, lexical and semantic search, hybrid retrieval and reranking
• LLM evaluation, observability, tracing and regression testing
• Cloud deployment, containerisation and operation of production AI services
• Degree in computer science, machine learning, AI, data science, mathematics, statistics or equivalent experience
• Experience in a regulated, security sensitive or data intensive enterprise environment is an advantage
• Fluent English, work permit or eligibility to work in Switzerland