At vector8 we design, develop and deliver production grade AI Solutions, leveraging our deep understanding and specialism in the Swiss Banking and Insurance sectors. Our solutions are varied depending on the problem we are solving, ranging from agentic systems to more traditional AI-powered solutions based on LLMs, classical ML or speech models. Every solution meets the same impeccable standard of performance, robustness and long-term maintainability.
This is a hands-on engineering role where you will:
- Design and build agents and AI workflows, including tool integration, orchestration, guardrails and human validation steps.
- Integrate LLMs, retrieval and classical ML or speech models with clients' existing systems and data.
- Make these solutions reliable in production through systematic evaluation, tracing, fallback strategies, and cost and latency control.
- Apply software engineering best practices: testing, CI/CD, modular design and documentation.
- Address the requirements of regulated industries, such as security, compliance, access control and integration with legacy systems.
- Own solutions end to end, from scoping with the client to production monitoring and continuous improvement.
- Work closely with vector8's engineers and project managers, as well as with client architects and business teams.
You will help build AI foundations that our clients can reuse and extend as they scale from individual use cases to company-wide capabilities.
The role is primarily based in Zurich, with occasional travel to client sites and collaboration with teams across Europe.
Responsibilities
- 5 + years of experience in AI/ML engineering, software development, or 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, transformers and classical ML models.
- Strong software engineering skills:
- Proficient in Python (FastAPI, Pydantic, asyncio, type hints)
- Experience with API development
- Familiarity with modern toolchains (Docker, Kubernetes, Terraform)
- Hands-on experience with LLM integrations:
- LLM providers
- Vector databases (Pinecone, Weaviate, Milvus)
- Model serving (vLLM, TGI, KServe)
- Experience with MLOps and production deployments
- Understanding of enterprise challenges:
- Security, compliance, scalability, cost optimization.
- Experience with relational and non-relational databases.
- Strong problem-solving and debugging skills.
- Bachelor’s or Master’s degree in Computer Science, Mathematics, Physics, or a related field.
- Experience with multi-cloud environments (AWS, Azure, GCP).
- Experience with code optimization (e.g., model quantization, parallelization).
- Excellent communication and collaboration skills – in this role you must be fluent in English and German. Swiss German is a plus.
Requirements
1. Agentic systems and AI workflows
- Design and build LLM agents that use tools and interact with enterprise systems and data
- Orchestrate multi-step and multi-agent workflows, with human validation and hand-off where needed
- Define clear boundaries for each agent through typed tool contracts, structured outputs, guardrails and access policies
- Connect agents to other systems through open protocols such as MCP and A2A, and through well-designed APIs
- Choose the most appropriate architecture for each problem, whether an agent, a deterministic workflow or a classical ML solution
- Evaluate AI solutions systematically against datasets to generate meaningful performance metrics.
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 (GitHub Actions, GitLab CI, Argo CD)
- 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. Enterprise AI & MLOps
- Deploy and monitor AI solution in production
- Design and implement MLOps pipelines for:
- Model training, fine-tuning, and evaluation
- Model versioning and lineage tracking
- A/B testing and canary deployments.
- Ensure scalability and reliability (auto-scaling, fault tolerance, disaster recovery).
- Collaborate with data engineers to build data pipelines (batch, streaming, real-time).
4. Collaboration & Technical Leadership
- Work closely with product owners, DevOps, and quality assurance in an agile, cross-functional team.
- Mentor junior engineers and promote best practices in AI/ML and software engineering.
- Translate product requirements into technical solutions and architectural decisions
- Document architectures, decisions, and best practices for internal and client-facing use.
- Develop relationships with internal and external stakeholders, including clients and partners.
5. End-to-End AI-powered Solutions Development (less frequent but can occur)
- 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 lifecycle:
- Data exploration and cleaning to build reproducible, versioned datasets
- State-of-the-art research to identify the best architectures for the problem (e.g., transformers, RAG, fine-tuning).
- Implementation, training, and optimization in reproducible environments
- Deployment, monitoring, and maintenance in production.
- Optimize models for performance, latency, and cost efficiency, especially in LLM serving and inference.
6. Innovation & Continuous Improvement
- Stay ahead of the latest AI and ML architectures (transformers, Mixture of Experts, sparse attention).
- Experiment with cutting-edge techniques (quantization, distillation, speculative decoding).
- Evaluate and benchmark open-source and proprietary models (Llama, Mistral, Mixtral, GPT-4, Claude).
- Bring your own ideas through vector8’s ideation process.
- Contribute to vector8’s AI accelerators (reusable components for common industry problems).
- Embrace a strategic and continuous improvement mentality to drive innovation.
Benefits
- A role at the forefront of AI transformation for leading Swiss enterprises in financial services, insurance, and beyond.
- Work with cutting-edge AI technologies and innovative solutions that create real, measurable business value.
- A dynamic, entrepreneurial work environment where your contributions directly drive company growth.
- Supportive team culture that prioritises continuous learning, professional development, and personal growth.
- A leadership ethos focused on empowering people, fostering collaboration, excellence, authenticity, and diversity.
- Competitive salary package, 25 days of vacation, development budget, and a flat hierarchy.
Why This Role is Unique - You will work at the intersection of AI research and enterprise software engineering, with a strong focus on AI-driven solutions.
- You will shape the future of AI adoption in Switzerland’s most complex organizations.
- You will bridge the gap between cutting-edge AI and real-world enterprise constraints (security, compliance, legacy systems).
- You will have the opportunity to mentor teams, drive innovation, and represent vector8 as a thought leader.