Senior AI​/ML Engineer, CH

As a Senior AI/ML Engineer at vector8, you will design, implement, and deploy AI solutions that bridge the gap between research and production. Your work will focus on integrating and fine-tuning AI Models, optimizing the model performance, and ensuring enterprise-grade reliability, security, and scalability.

This is a hands-on engineering role where you will:

• Develop and optimize
  
  LLMand VLM-powered solutions for enterprise use cases
• Develop and optimize
  
  TTS, STT and ML models.
• Collaborate with cross-functional teams (data engineers,MLOps, cloud architects, and business stakeholders)
• Solve real-world enterprise challenges (security, compliance, legacy system integration)
• Own the full lifecycle of AI models, from data exploration to production monitoring

You will work closely with vector8’s Engineers and Project Managers to co-design AI foundations that enable organizations to scale AI from individual use cases to enterprise-wide capabilities.

The role is primarily based in Zurich, with occasional travel to client sites and collaboration with teams across Europe.

• 5+ years of experience in AI/ML engineering, software development, or a related field
• Expertise in LLM architectures and training methodologies:
  • Transformers, attention mechanisms, fine-tuning, RAG, quantization
  • Prompt engineering, model evaluation, bias detection
• Strong knowledge of machine learning architectures: fully connected, CNN, LSTM,transformersand classical ML models
• Strong software engineering skills:
  • Proficient in Python (FastAPI,Pydantic,asyncio, type hints)
  • Experience with API development
  • Familiarity with modern tool chains (Docker, Kubernetes, Terraform)
• Hands-on experience with LLM integrations:
  • LLM providers
  • Vector databases (Pinecone,Weaviate, Milvus)
  • Model serving (vLLM, TGI,KServe)
• Experience with
  
  MLOpsand production deployments
• Understanding of enterprise challenges:
  • Security, compliance, scalability, cost optimization.
• Experience with relational and non-relational databases.
• Strong problem-solving and debugging skills.
• Excellent communication and collaboration skills (fluent in English; German is a strong plus).
• Bachelor’s or Master’s degree in Computer Science, Mathematics, Physics, ora related field.
• Experience with multi-cloud environments (AWS, Azure, GCP).
• Experience with code optimization (e.g., model quantization, parallelization).

1. End-to-End Model Development

• Design, implement, and deploy distributed, high-volume, high-performance, low-latency machine learning solutions, with a focus on GenAI models, and especially LLM integrations and API-driven architectures
• Take ownership of your models throughout their entire life cycle:
• Data exploration and cleaning to build reproducible, versioned datasets
• State-of-the-artresearch toidentifythe best architectures for the problem (e.g., transformers, RAG, fine-tuning)
• Implementation, training, and optimization in reproducible environments
• Deployment, monitoring, and maintenance in production
• Optimizemodels for performance, latency, and cost efficiency, especially in LLM serving and inference

2. Software Engineering for AI

• Write clean, modular, and well-documented code in Python (FastAPI,Pydantic,asyncio)
• Apply best practices in:
  • Testing (unit, integration, end-to-end)
  • CI/CD (Git Hub Actions, Git Lab CI,ArgoCD)
  • Observability (logging, monitoring, tracing)
• Ensure security and compliance (data protection, access controls, encryption)
• Integrate models and code into CI/CD pipelines for seamless deployment

3.AI& MLIntegration & API Development

• Design and implement
  
  AI-powered solutions that integrate with APIs, microservices, and event-driven architectures
• Develop and optimize
  
  AIpipelines for:
  • Dataset cleaning,preprocessingand model training
  • Fine-tuning (domain adaptation, instruction tuning)
  • Retrieval-Augmented Generation (RAG) (vector databases, semantic search)
  • Prompt engineering (optimizinginputs for performance, cost, and accuracy)
• Build scalable, secure, and cost-efficient serving infrastructure (e.g.,FastAPI,vLLM)
• Debug andoptimizeperformance (latency, throughput, token efficiencyfor Transformer based architectures)
• Deploy and monitor
  
  AI models in production
• Design and implement
  
  MLOpspipelines…