Senior AI/ML Engineer, CH

vector8; Zürich; Vor Ort; CHF 90.000 - 120.000; Vollzeit

Benefits dieser Stelle

Competitive salary package

25 days of vacation

Development budget

Dynamic work environment

Zusammenfassung

A leading AI solutions provider in Zurich is seeking a Senior AI/ML Engineer to design and implement cutting-edge AI solutions. In this hands-on role, you will focus on integrating and optimizing AI models while ensuring enterprise-grade reliability and scalability. The ideal candidate has over 5 years of experience in AI/ML engineering and software development. If you’re passionate about driving innovation in AI, apply now to make a real impact on enterprise challenges.

Qualifikationen

• 5+ years of experience in AI/ML engineering, software development, or a related field.
• Strong knowledge of LLM architectures and training methodologies.
• Hands-on experience with AI/ML technology integrations.

Aufgaben

• Design, implement, and deploy high-performance machine learning solutions.
• Collaborate with cross-functional teams to integrate AI solutions.
• Mentor junior engineers and document architectural decisions.

Kenntnisse

AI/ML engineering

Software development

Python

API development

Problem-solving

Excellent communication

Ausbildung

Bachelor’s or Master’s degree in Computer Science, Mathematics, Physics, or related field

Tools

FastAPI

Pydantic

Docker

Kubernetes

Terraform

Jobbeschreibung

Job Description

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 model performance, and ensuring enterprise‑grade reliability, security, and scalability.

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

This Is a Hands‑on Engineering Role Where You Will

• Develop and optimize LLM and VLM‑powered solutions for enterprise use cases
• Develop and optimize TTS, STT and ML models.
• Apply software engineering best practices (testing, CI/CD, modular design, documentation)
• 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

Job Requirements

• 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, 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
• Excellent communication and collaboration skills (fluent in English; German is a strong plus)
• 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).

Job Responsibilities

• 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‑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
• 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, 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
• AI & ML Integration & API Development
  • Design and implement AI‑powered solutions that integrate with APIs, microservices, and event‑driven architectures
  • Develop and optimize AI pipelines for:
    • Dataset cleaning, preprocessing and model training
    • Fine‑tuning (domain adaptation, instruction tuning)
    • Retrieval‑Augmented Generation (RAG) (vector databases, semantic search)
    • Prompt engineering (optimizing inputs for performance, cost, and accuracy)
    • Model evaluation (benchmarking, bias detection, drift analysis)
    • Build scalable, secure, and cost‑efficient serving infrastructure (e.g., FastAPI, vLLM)
    • Debug and optimize performance (latency, throughput, token efficiency for Transformer‑based architectures)
  • Enterprise AI & MLOps
    • Deploy and monitor AI models 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)
• 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
• 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

Job 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