AI Engineer – LLM Specialist

Salary: CHF 100'000 - 130'000 per year

Requirements:

• What You Bring
• AI / ML Experience
• At least 3–5 years of experience in machine learning or applied AI.
• Practical experience working with LLMs in production or advanced prototypes.
• Model Training & Fine-Tuning
• Experience with PyTorch or TensorFlow.
• Familiarity with fine-tuning techniques and training pipelines.
• Evaluation & Experimentation
• Strong understanding of experimental design.
• Experience building evaluation harnesses.
• Programming Skills
• Strong Python skills.
• Familiarity with REST APIs and backend integration.
• Data Handling & MLOps
• Experience with dataset preprocessing, labeling pipelines, and versioning.
• Familiarity with Docker, CI/CD, and model deployment.
• Analytical Mindset
• Ability to reason about model behavior and failure modes.
• Communication
• Good verbal and written communication in English and German.
• Startup Mentality
• Comfortable with ambiguity, fast iteration, and high ownership.

Responsibilities:

• Key Responsibilities
• LLM Evaluation & Testing
• Design and maintain systematic evaluation frameworks for LLMs, including: Automated test suites, Golden datasets, Regression benchmarks
• Define quantitative metrics (e.g., accuracy, latency, hallucination rate, task success) and qualitative evaluation protocols.
• Perform error analysis and root-cause investigations on model failures.
• Task Alignment & Optimization
• Focus on rapid prototyping and operationalization of customer use cases
• Improve model performance on specific tasks using a prompt-first workflow (system prompts, few-shot examples, tool instructions).
• Build and iterate evaluation sets; run experiments to measure quality, latency, and cost.
• Curate high-signal datasets for automated prompt optimization (cleaning, labeling, filtering, augmentation).
• Apply lightweight adaptation when beneficial (prompt tuning, parameter-efficient methods like LoRA/adapters).
• Use supervised fine-tuning / instruction tuning when prompting and lightweight methods don’t reach the target.
• Prepare and curate training datasets (cleaning, labeling, augmentation, filtering).
• Model Selection & Experimentation
• Evaluate and compare open-source and commercial LLMs for specific use cases.
• Design controlled experiments (A/B tests, offline evaluations).
• Document results and recommend model choices.
• Integration into Product
• Collaborate with full-stack engineers to integrate prototypes into product, backend services and user-facing applications.
• Support API design for model inference and post-processing.
• Ensure models behave reliably in real-time and batch workflows.
• Quality, Safety & Guardrails
• Implement mechanisms to:
• Reduce hallucinations
• Enforce output formats
• Apply content filters
• Detect and handle unsafe or low-confidence outputs
• Performance & Cost Optimization
• Optimize inference latency and throughput.
• Balance model size, quantization, batching, and caching strategies.
• Monitor and optimize inference costs.
• MLOps & Lifecycle Management
• Version models, datasets, prompts, and evaluation results.
• Support deployment pipelines for new model versions.
• Monitor model performance in production and detect drift.
• Collaboration & Knowledge Sharing
• Work closely with product managers to translate requirements into model behaviors.
• Support internal teams with guidance on prompt design and model usage.
• Contribute to documentation and internal best practices.
• Dataset Strategy & Governance
• Define standards for dataset quality, labeling guidelines, and storage.
• Maintain traceability between datasets, experiments, and deployed models.
• Synthetic Data Generation
• Use LLMs or other techniques to generate synthetic training data where real data is scarce.
• Agentic LLMs & Human-in-the-Loop Workflows
• Design and test LLM workflows that call tools, functions, or external APIs.
• Design feedback loops where human reviewers validate or correct model outputs.
• Research Scouting
• Track relevant papers, frameworks, and open-source projects.
• Prototype promising techniques quickly.
• Internal Enablement
• Create internal guidelines for prompt writing and evaluation.
• Run occasional knowledge-sharing sessions.

Technologies:

• AI
• API
• Backend
• CI/CD
• Docker
• Support
• LLM
• Machine Learning
• PyTorch
• Python
• REST
• TensorFlow
• Cursor
• GitHub
• Slack

More:

What We Are Offering

Opportunity to participate in AlpineAI’s company shares program after initiation period. Dynamic, innovation-driven culture. High autonomy and real product impact. Close collaboration with experts in speech, NLP, and applied AI. Exposure to cutting-edge AI technologies. On-site role in Zurich or Davos

Don’t Apply If

You are not willing to work on-site in Zurich or Davos. You do not have a work permission for Switzerland. You have never worked in a startup environment.

About Us

Learn more about AlpineAI at: https://alpineai.swiss

Ready to help customers succeed with AI?

Apply now with your CV and a short cover letter. We look forward to hearing from you.

last updated 14 week of 2026