Senior AI Engineer

DatAfterwork; Schweiz; Hybrid; EUR 148.000 - 201.000; Vollzeit

Zusammenfassung

DatAfterwork is assisting an established Swiss consulting firm in IT, Data and AI, based in French-speaking Switzerland and serving clients across Switzerland. You will join a growing Data & AI practice and take end-to-end ownership of AI solutions for clients, from use-case discussions through to production. The role blends hands-on engineering with client-facing consulting.

Location: French-speaking Switzerland, hybrid work possible. Start date as soon as possible.

Qualifikationen

• 4 to 5 years of experience in AI or ML engineering, data science or a closely related role.

Aufgaben

• Design, build and deploy LLM-based and agentic solutions in production, taking them from prototype to industrialised service.

Jobbeschreibung

We are supporting an established Swiss consulting firm in IT, Data and AI, based in French-speaking Switzerland and serving clients across Switzerland. The firm is known for the quality and reliability of its delivery and for long-term relationships with its clients.

Join a growing Data & AI practice. This is a genuinely intrapreneurial position: you will deliver on flagship engagements while helping shape the practice’s methods, tooling, standards and offering, working directly with the practice lead.

As Senior AI Engineer, you take end-to-end ownership of the AI solutions designed and deployed for clients, from first use case discussions through to a secured, monitored service running in production. The role is hands-on and technical, with a strong consulting dimension: you will spend a significant part of your time with clients, understanding their needs, explaining trade-offs and presenting results.

LLM & Agentic Systems in Production

Harness Engineering & Evaluation

MLOps & Security

Consulting Posture & Co-building Mindset

Main Responsibilities

• Design, build and deploy LLM-based and agentic solutions in production (internal assistants, retrieval systems, document intelligence pipelines), taking them from prototype to industrialised service.


• Build fast and reliable retrieval (RAG) over large and heterogeneous corpora (PDF, office documents, emails, structured data), with constant attention to latency, relevance and answer quality.


• Engineer the harness around the model: context and memory management, tool and MCP integration, retries and fallbacks, token and cost budgeting, tracing and evaluation.


• Secure what you ship: access control, data protection and confidentiality, defences against prompt injection and data leakage, auditability, and compliance with client and Swiss data requirements.


• Own the MLOps lifecycle: CI/CD for models and prompts, evaluation frameworks, observability and monitoring, cost and performance tracking.


• Work directly with clients: run discovery workshops, translate business needs into technical designs, present options and results to both business and technical stakeholders.


• Contribute to building the practice: reusable accelerators, technical standards and best practices, knowledge sharing, technical input on proposals and pre-sales.


• Collaborate with the strategy, change management, analytics and data science consultants as the team grows, and mentor future technical hires.

Profile

• 4 to 5 years of experience in AI or ML engineering, data science or a closely related role, in a consulting firm or in industry.


• A data background is our preference: you understand that AI fails on data before it fails on models. Other strong engineering backgrounds are welcome.


• Expert-level Python: clean, tested, production-grade code is your default.


• Proven production experience: you have already deployed at least one internal AI agent or LLM application that is in real use, and you know what it takes to keep it fast, secure and reliable.


• LLM application engineering: RAG architecture, embeddings and vector or hybrid search, prompt and context design, tool and function calling, fine-tuning where it is warranted.


• Agentic systems: orchestration frameworks such as LangChain/LangGraph, Semantic Kernel, AutoGen or CrewAI; multi-step workflows, memory and tool integration.


• Harness engineering: context and memory management, tool and MCP integration, retries and fallbacks, token and cost budgeting, tracing and evaluation harnesses around the model.


• Evaluation and reliability: you know how to measure quality without ground truth, reduce hallucination, and prove that a system is fit for business use.


• Solid MLOps practice: containers, at least one major cloud platform (Azure, AWS or GCP), data and model pipelines, versioning, testing and monitoring.


• Strong data science and NLP foundations: LLM APIs and open-weight models, classical NLP, evaluation methods.


• Document and file processing: parsing, OCR, chunking strategies, structured information extraction from unstructured sources.


• Security mindset: you design for confidentiality, access rights and traceability from the start, not as an afterthought.


• Consulting posture: structured, client-facing, comfortable presenting to senior stakeholders and explaining technical choices in business terms.


• Sector exposure: experience in FMCG or consumer goods is a strong plus; experience with international organisations or other regulated, multi-stakeholder environments is also valued.


• Languages: fluent French and English.


• Mindset: intrapreneurial, autonomous and curious, with a real appetite for co-building a practice, sharing knowledge and growing with a team.

Nice to Have

• Hands-on experience with one or more enterprise AI stacks: Microsoft (Azure AI Foundry, Azure OpenAI, Fabric, Copilot Studio), Databricks, Snowflake or Dataiku.


• Open-source contributions, technical writing or public work that shows how you think and build. If you have a GitHub profile or Stack Overflow activity you are proud of, share the links with your application.

Conditions

• Location: French-speaking Switzerland (Lake Geneva area), with regular presence at client sites; hybrid arrangement possible


• Start date: As soon as possible


• Compensation: Competitive, in line with the Swiss consulting market

Why Join

• A key role in a strategic, growing practice, inside an established Swiss firm with a solid client base and reputation.


• Direct exposure to the practice lead and executive committee, and real influence on how the practice is built.


• Flagship engagements with high-profile clients from day one.


• A clear growth path as the team scales, including technical leadership of the AI engineering stream.

Recruitment Process

• 1. Pre-qualification interview with Candice Valembois, Valembois Consulting (video call or on-site interview, approx. 45 minutes)


• 2. Interview with the Managing Director & Head of Data & AI


• 3. Business case: a short case combining technical design and business reasoning, presented to the practice lead


• 4. Interview with the HR Director


• 5. Reference checks, followed by an offer


• The name of the company is disclosed to shortlisted candidates only. All applications are treated in strict confidence.