AI Engineer – LLM Specialist ML, AI 🏆

AI Engineer – LLM Specialist

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

At AlpineAI AG we are looking for a ML, AI engineer!

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Tech Stack

• GitHub
• Slack
• Cursor
• Machine-Learning

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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.

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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.

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Benefits & Perks

• 5 weeks (25 days) vacation
• Team social events
• Equity or company stock
• Home office / Remote 2 days per week
• Hybrid work

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About AlpineAI

• 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

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

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Application

Ready to help customers succeed with AI?

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

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Location

Obere Strasse 22b, Chur, Switzerland

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Salary

CHF 100’000 - 130’000 per year

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Category

ML, AI Developer / Engineer

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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.

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Additional Information

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