Who is Artefact?

Artefact is a next-generation strategy and data consulting firm dedicated to transforming organizations through data and AI. We combine the rigor of top-tier strategy consulting with deep expertise in data, digital, and analytics to help clients achieve tangible business impact.

With 1,800+ consultants, data scientists, and engineers across 23 countries, we work with global leaders such as Samsung, L’Oréal, Orange, and Sanofi. Our Romandie office (Geneva/Lausanne) is at the heart of Artefact’s growth, advising clients on their most pressing strategic challenges — from AI strategy and governance to digital transformation roadmaps and new business model design.

Main Responsibilities

As a Senior AI & Data Engineer, you will be the architect of the "AI Factory." Your role is less about training models from scratch and more about building the industrial-grade pipelines, infrastructure, and security frameworks required to run AI in a highly regulated environment.

• Production-Grade AI Infrastructure: Design and implement robust MLOps and LLMOps pipelines specifically for banking environments, focusing on data residency, air-gapping possibilities, and high availability.

• Data Engineering for AI: Build scalable ETL/ELT pipelines to feed RAG (Retrieval-Augmented Generation) systems, ensuring data lineage, quality, and strict access control (RBAC).

• DevOps & Automation: Own the CI/CD lifecycle for AI assets. Automate the deployment of model APIs, vector databases, and monitoring stacks using Infrastructure as Code (IaC).

• Hybrid Cloud & On-Prem: Navigate complex hybrid-cloud architectures (Azure/AWS/GCP vs. Private Cloud) common in Swiss banking.

• Technical Advisory: Act as a bridge between IT Infrastructure, Risk/Compliance, and Business units to translate AI potential into stable, governed reality.

Candidate Profile

Engineer first, consultant second — with a builder’s mindset and a track record of shipping.

• Experience: 4+ years in Data Engineering, DevOps, or Software Engineering, with at least 2 years focused on deploying Machine Learning or AI systems at scale.

• The "DevOps" Stack: Expert knowledge of Docker, Kubernetes (K8s), and CI/CD tools (GitHub Actions, GitLab CI, or Jenkins). Experience with Terraform or Pulumi is a strong plus.

• The "Data" Stack: Advanced SQL and Python. Deep experience with data orchestration (Airflow, Dagster), streaming (Kafka), and modern data warehouses (Snowflake, Databricks).

• AI/LLM Implementation: Hands-on experience with the "plumbing" of AI: Vector databases (Milvus, Qdrant, or pgvector), model serving (BentoML, vLLM), and RAG orchestration (LangChain/LlamaIndex).

• Banking Domain: Solid understanding of financial data structures and the regulatory landscape (Model Risk Management, Audit trails, Data anonymization).

• Languages: English is mandatory. German (B2 or higher) is a significant advantage for the Zurich market and stakeholder management.

• Education: Master’s degree in Computer Science, Software Engineering, or a related quantitative field.

Nice to have

• Experience with responsible AI/governance frameworks, security reviews, and cost optimization.

• Domain experience in finance.

• Contributions to open-source, publications, or conference talks.

Why Join Us

• Strategy with a Data Edge: Operate at the intersection of boardroom strategy and cutting-edge AI engineering.

• Zurich Office Impact: High visibility from day one; help shape our Swiss footprint and work directly with senior leadership.

• Learning & Growth: Advanced training across strategy, AI/ML/LLM, and cloud; international missions and communities of practice.

• Culture of Doers: Innovation, action, collaboration. We move fast, deliver impact, and support each other’s growth.

Location: Zurich

If you don’t meet 100% of the criteria, we still want to hear from you. Passion, curiosity, and impact orientation matter — tell us about yours.

Who is Artefact?

Artefact is a next-generation strategy and data consulting firm dedicated to transforming organizations through data and AI. We combine the rigor of top-tier strategy consulting with deep expertise in data, digital, and analytics to help clients achieve tangible business impact.

With 1,800+ consultants, data scientists, and engineers across 23 countries, we work with global leaders such as Samsung, L’Oréal, Orange, and Sanofi. Our Romandie office (Geneva/Lausanne) is at the heart of Artefact’s growth, advising clients on their most pressing strategic challenges — from AI strategy and governance to digital transformation roadmaps and new business model design.

Main Responsibilities

As a Senior AI & Data Engineer, you will be the architect of the "AI Factory." Your role is less about training models from scratch and more about building the industrial-grade pipelines, infrastructure, and security frameworks required to run AI in a highly regulated environment.

• Production-Grade AI Infrastructure: Design and implement robust MLOps and LLMOps pipelines specifically for banking environments, focusing on data residency, air-gapping possibilities, and high availability.

• Data Engineering for AI: Build scalable ETL/ELT pipelines to feed RAG (Retrieval-Augmented Generation) systems, ensuring data lineage, quality, and strict access control (RBAC).

• DevOps & Automation: Own the CI/CD lifecycle for AI assets. Automate the deployment of model APIs, vector databases, and monitoring stacks using Infrastructure as Code (IaC).

• Hybrid Cloud & On-Prem: Navigate complex hybrid-cloud architectures (Azure/AWS/GCP vs. Private Cloud) common in Swiss banking.

• Technical Advisory: Act as a bridge between IT Infrastructure, Risk/Compliance, and Business units to translate AI potential into stable, governed reality.

Candidate Profile

Engineer first, consultant second — with a builder’s mindset and a track record of shipping.

• Experience: 4+ years in Data Engineering, DevOps, or Software Engineering, with at least 2 years focused on deploying Machine Learning or AI systems at scale.

• The "DevOps" Stack: Expert knowledge of Docker, Kubernetes (K8s), and CI/CD tools (GitHub Actions, GitLab CI, or Jenkins). Experience with Terraform or Pulumi is a strong plus.

• The "Data" Stack: Advanced SQL and Python. Deep experience with data orchestration (Airflow, Dagster), streaming (Kafka), and modern data warehouses (Snowflake, Databricks).

• AI/LLM Implementation: Hands-on experience with the "plumbing" of AI: Vector databases (Milvus, Qdrant, or pgvector), model serving (BentoML, vLLM), and RAG orchestration (LangChain/LlamaIndex).

• Banking Domain: Solid understanding of financial data structures and the regulatory landscape (Model Risk Management, Audit trails, Data anonymization).

• Languages: English is mandatory. German (B2 or higher) is a significant advantage for the Zurich market and stakeholder management.

• Education: Master’s degree in Computer Science, Software Engineering, or a related quantitative field.

Nice to have

• Experience with responsible AI/governance frameworks, security reviews, and cost optimization.

• Domain experience in finance.

• Contributions to open-source, publications, or conference talks.

Why Join Us

• Strategy with a Data Edge: Operate at the intersection of boardroom strategy and cutting-edge AI engineering.

• Zurich Office Impact: High visibility from day one; help shape our Swiss footprint and work directly with senior leadership.

• Learning & Growth: Advanced training across strategy, AI/ML/LLM, and cloud; international missions and communities of practice.

• Culture of Doers: Innovation, action, collaboration. We move fast, deliver impact, and support each other’s growth.

Location: Zurich

If you don’t meet 100% of the criteria, we still want to hear from you. Passion, curiosity, and impact orientation matter — tell us about yours.

Your impact 

As a Senior Security Engineer, you will architect and manage the security of our groundbreaking ML-based platform and High Performance Computing (HPC) infrastructure. This role requires a highly proactive problem-solver who enjoys a fast-paced environment and possesses the curiosity to dive into diverse technical challenges. 

You will act as a versatile, T-shaped engineer, overseeing security solutions from initial requirement gathering to final implementation. By combining deep technical know-how with strong collaborative skills, you will ensure our security posture evolves as quickly as our research, managing the end-to-end lifecycle of our defense systems.

What you will do

• Secure Architecture and Product Engineering: Participate in the design and perform security reviews of our evolving AI platforms and underlying HPC infrastructure.
• Infrastructure as Code (IaC) Security: Partner with our DevOps / SRE team to harden our cloud infrastructure and our network, ensuring security by design, automation and auditability through Policy as Code.
• Third Party Systems Secure Integration: Perform deep-dive technical assessments of third-party platforms, AI solutions, Cloud or SaaS providers and support secure integration or deployment.
• Secure CI/CD: Design and implement automated security controls within our CI/CD pipelines to ensure code is secure from commit to production without slowing down research velocity.
• Threat Modeling & Risk Assessment: Conduct proactive threat modeling and risk assessment, support teams in the implementation of remediation plan and audit expected outcomes.
• Incident Response: Act as a L2/L3 escalation point for the remediation of complex vulnerabilities and security incidents.
• Identity & Access Management: Implement our state-of-the-art Zero Trust framework, ensuring robust access control and consistent enforcement of the principle of least privilege.
• Risk Management and Compliance Automation: Bridge the gap between technical controls and regulatory requirements (GDPR, GxP, EU AI Act) by automating evidence collection and risk posture monitoring (CSPM).
• Security Tooling Development: Build or integrate custom internal tools that automate repetitive security tasks, shifting our operational load from manual toil to scalable engineering.
• End-to-End Solution Delivery: Manage the full lifecycle of security controls, from initial user needs analysis and requirements gathering to structured testing and phased implementation and communication, ensuring high-quality deployment followed by data-driven continuous improvement.

Skills and qualifications 

Essential

• Cloud Engineering Proficiency: Deep technical knowledge of cloud platform security (GCP preferred) including Network and VPC design, IAM policy construction, Cloud resources hardening and Cloud native security services.
• Analytical Risk Management and Problem Solving: Proficiency in assessing multi-faceted risks and decomposing complex security issues into manageable tasks and providing data-driven recommendations to stakeholders.
• Coding Skills: Ability to write small production-grade code (e.g. in Python) and to automate security tasks, build custom tooling, etc.
• DevSecOps Tooling: Hands-on experience with Infrastructure as Code (Terraform) and version control systems (GitHub) to manage security configurations.
• Container Security: Proven ability to secure containerized workloads (Kubernetes/Docker), focusing on image signing, runtime protection, and orchestration security.
• Network Security Fundamentals: Solid understanding of modern networking, including zero-trust architecture, encryption in transit (TLS/mTLS), and API gateway security.
• Identities and Access Management: Proficiency in implementing a state of the art IAM strategy both from an organisational and technical standpoints in a multi-tenant cloud environment.
• Collaborative Security Culture: Strong ability to support researchers in AI and Drug Discovery, leveraging excellent listening skills, to provide pragmatic advice that balances high-security requirements with business agility.
• Adaptability & Communication: Excellent soft skills with the ability to navigate an ambiguous, high-growth environment and explain technical risks to non-security audiences.
• Offensive Mindset: Strong understanding of the MITRE ATT&CK framework and the ability to think like an adversary to identify "blind spots" in our defense.

Nice to have

• AI/ML Security Interest: Familiarity with the unique security challenges of an AI first company and other common AI solutions such as LLMs.
• Regulated Industry Experience: Prior experience working in BioTech, Pharma where data integrity and regulatory compliance are paramount.
• Advanced Security Certifications: Holding industry-recognized credentials such as GSE, OSCP, CISSP or professional-level Cloud Security Engineer certifications.
• Application Security (AppSec): Experience with SAST/DAST/SCA tools and a strong understanding of the OWASP Top 10 vulnerabilities.
• Zero Trust Implementation: Past success in transitioning an organization away from traditional perimeter-based security toward a mature Zero Trust model.
• SecOps Maturity: Experience building or scaling a Security Operations Center (SOC) or a Modern Detection and Response (MDR) function.
• Collaboration Tool Mastery: Advanced experience securing and automating SaaS. In particular, Google Workspace, the Atlassian stack (Jira/Confluence), Slack.
• Bio-Pharma Experience: Prior exposure to GxP validation, clinical trial data protections, or the nuances of Lab-IT security.
• Privacy Engineering: Knowledge of PETs (Privacy Enhancing Technologies) like differential privacy or homomorphic encryption.

Your impact 

As a Senior Security Engineer, you will architect and manage the security of our groundbreaking ML-based platform and High Performance Computing (HPC) infrastructure. This role requires a highly proactive problem-solver who enjoys a fast-paced environment and possesses the curiosity to dive into diverse technical challenges. 

You will act as a versatile, T-shaped engineer, overseeing security solutions from initial requirement gathering to final implementation. By combining deep technical know-how with strong collaborative skills, you will ensure our security posture evolves as quickly as our research, managing the end-to-end lifecycle of our defense systems.

What you will do

• Secure Architecture and Product Engineering: Participate in the design and perform security reviews of our evolving AI platforms and underlying HPC infrastructure.
• Infrastructure as Code (IaC) Security: Partner with our DevOps / SRE team to harden our cloud infrastructure and our network, ensuring security by design, automation and auditability through Policy as Code.
• Third Party Systems Secure Integration: Perform deep-dive technical assessments of third-party platforms, AI solutions, Cloud or SaaS providers and support secure integration or deployment.
• Secure CI/CD: Design and implement automated security controls within our CI/CD pipelines to ensure code is secure from commit to production without slowing down research velocity.
• Threat Modeling & Risk Assessment: Conduct proactive threat modeling and risk assessment, support teams in the implementation of remediation plan and audit expected outcomes.
• Incident Response: Act as a L2/L3 escalation point for the remediation of complex vulnerabilities and security incidents.
• Identity & Access Management: Implement our state-of-the-art Zero Trust framework, ensuring robust access control and consistent enforcement of the principle of least privilege.
• Risk Management and Compliance Automation: Bridge the gap between technical controls and regulatory requirements (GDPR, GxP, EU AI Act) by automating evidence collection and risk posture monitoring (CSPM).
• Security Tooling Development: Build or integrate custom internal tools that automate repetitive security tasks, shifting our operational load from manual toil to scalable engineering.
• End-to-End Solution Delivery: Manage the full lifecycle of security controls, from initial user needs analysis and requirements gathering to structured testing and phased implementation and communication, ensuring high-quality deployment followed by data-driven continuous improvement.

Skills and qualifications 

Essential

• Cloud Engineering Proficiency: Deep technical knowledge of cloud platform security (GCP preferred) including Network and VPC design, IAM policy construction, Cloud resources hardening and Cloud native security services.
• Analytical Risk Management and Problem Solving: Proficiency in assessing multi-faceted risks and decomposing complex security issues into manageable tasks and providing data-driven recommendations to stakeholders.
• Coding Skills: Ability to write small production-grade code (e.g. in Python) and to automate security tasks, build custom tooling, etc.
• DevSecOps Tooling: Hands-on experience with Infrastructure as Code (Terraform) and version control systems (GitHub) to manage security configurations.
• Container Security: Proven ability to secure containerized workloads (Kubernetes/Docker), focusing on image signing, runtime protection, and orchestration security.
• Network Security Fundamentals: Solid understanding of modern networking, including zero-trust architecture, encryption in transit (TLS/mTLS), and API gateway security.
• Identities and Access Management: Proficiency in implementing a state of the art IAM strategy both from an organisational and technical standpoints in a multi-tenant cloud environment.
• Collaborative Security Culture: Strong ability to support researchers in AI and Drug Discovery, leveraging excellent listening skills, to provide pragmatic advice that balances high-security requirements with business agility.
• Adaptability & Communication: Excellent soft skills with the ability to navigate an ambiguous, high-growth environment and explain technical risks to non-security audiences.
• Offensive Mindset: Strong understanding of the MITRE ATT&CK framework and the ability to think like an adversary to identify "blind spots" in our defense.

Nice to have

• AI/ML Security Interest: Familiarity with the unique security challenges of an AI first company and other common AI solutions such as LLMs.
• Regulated Industry Experience: Prior experience working in BioTech, Pharma where data integrity and regulatory compliance are paramount.
• Advanced Security Certifications: Holding industry-recognized credentials such as GSE, OSCP, CISSP or professional-level Cloud Security Engineer certifications.
• Application Security (AppSec): Experience with SAST/DAST/SCA tools and a strong understanding of the OWASP Top 10 vulnerabilities.
• Zero Trust Implementation: Past success in transitioning an organization away from traditional perimeter-based security toward a mature Zero Trust model.
• SecOps Maturity: Experience building or scaling a Security Operations Center (SOC) or a Modern Detection and Response (MDR) function.
• Collaboration Tool Mastery: Advanced experience securing and automating SaaS. In particular, Google Workspace, the Atlassian stack (Jira/Confluence), Slack.
• Bio-Pharma Experience: Prior exposure to GxP validation, clinical trial data protections, or the nuances of Lab-IT security.
• Privacy Engineering: Knowledge of PETs (Privacy Enhancing Technologies) like differential privacy or homomorphic encryption.

Your Impact

We are seeking a Team / Research Leader specialising in computational chemistry to spearhead the design of novel small molecule therapeutics. This role requires a unique blend of deep scientific expertise and the ability to lead both technical projects and high-performing teams, whilst contributing to or leading active drug discovery programs.

As a pivotal member of the Drug Design team, you will drive complex projects from early discovery through to candidate selection. You will leverage your expertise in strategic leadership and management of multi-program pipelines to deliver life-changing medicines using our cutting-edge AI drug design engine.


What You Will Do

• Strategic Direction: Define the core scientific and technical direction of our small molecule therapeutic programs, ensuring alignment with company goals.
• Program Leadership: Champion the drug discovery pipeline, overseeing external collaborations (with CROs) and leading programs to meet critical go/no-go milestones.
• Innovation & Integration: Establish principles for embedding cutting-edge AI/ML into modern computational chemistry workflows, ensuring seamless integration across ML, Biology, and Chemistry teams.
• Talent Development: Foster a culture of scientific excellence by proactively mentoring, guiding, and evaluating the career development of team members.
• Technical Execution: Apply AI-first approaches to provide technical and strategic directions, and execute drug design programs.
  
  

Skills and Qualifications

Essential:

• PhD in Computational Chemistry, Medicinal Chemistry, Synthetic Chemistry, or a related scientific field.
• 8+ years’ professional experience in either a pharma, biotech or CRO environment working at all stages of the drug discovery process, from hit finding to lead optimisation and candidate selection.
• Proven experience leading computational chemistry strategy and execution for multiple drug discovery programs and target classes, and managing high-performing scientific teams.
• Strong expertise in virtual screening, ligand and structure-based drug design using physics-based and ideally AI/ML methods.
• Proven track record of developing QSAR models using ML or deep learning methods.
• Expertise in writing and implementing Python scripts for analysis and tool development, and/or utilising pipelining tools to support cheminformatics and design workflows.
• Strong knowledge of medicinal chemistry principles including ADMET and DMPK.
• Genuine passion and enthusiasm for applying AI/ML to drug design and how this can transform the field.
• Demonstrated success managing external collaborations (industry/CROs) and working effectively in cross-functional environments.
• Growth mindset, adaptability, and eagerness to learn new concepts and techniques.

Nice to Have:

• Prior experience leveraging machine learning and generative AI methods for hit finding (e.g. ultra-large virtual screening and active learning), molecular design and lead optimisation (e.g. REINVENT, cofolding and AI-powered ligand/pose scoring methods).
• Prior experience of leading drug discovery programs.
• Track record of authorship of relevant scientific manuscripts.
• Knowledge of modalities such as PROTACs, molecular glues, PPI inhibitors or ADCs.