Key Skills

Job Description

About The Role As a Frontier Engineer, you'll help build AI that moves the world forward. Working at the frontier of technology, you'll join a lean, high-impact pod where human ingenuity and AI come together to solve real-world challenges for some of the world's leading organizations. You'll partner directly with clients to design, deploy and continuously improve AI-native systems, with the freedom to experiment with emerging technologies and turn bold ideas into real impact. In this role, you will Build and orchestrate multi-agent AI systems, integrating agent outputs into production environments that solve real business problems. Design intent-driven prompts and workflows for AI coding agents, applying human judgment to validate quality, safety and accuracy. Create scalable RAG architectures, vector stores and knowledge systems that enable trustworthy and context-aware AI experiences. Deploy, monitor and optimize LLMs and AI agents in production, implementing responsible AI guardrails and observability practices. Build AI-embedded applications and workflows using enterprise AI platforms, automation tools and modern engineering practices. Work model We believe hybrid work is the way forward as we strive to provide flexibility wherever possible. Based on this role's business requirements, this is a hybrid position requiring 3 days a week in a client or Cognizant office in the Netherlands. Regardless of your working arrangement, we are here to support a healthy work-life balance through our various wellbeing programs. The working arrangements for this role are accurate as of the date of posting. This may change based on the project you're engaged in, as well as business and client requirements. Rest assured; we will always be clear about role expectations. What You Must Have To Be Considered 3-10 years of technical delivery experience with strong AI-tool fluency across the full stack. Hands-on experience with agentic coding and multi-agent orchestration using frameworks such as LangChain, AutoGen or LangGraph. Strong prompt and intent engineering skills, with the ability to validate AI-generated code for quality and correctness. Experience designing RAG pipelines and managing vector databases to ground and improve agent accuracy. LLMOps and model lifecycle management experience, including guardrail implementation, AIOps observability and CI/CD for AI systems. These will help you succeed Demonstrated agentic engineering and orchestration expertise paired with strong context and retrieval engineering. Production AI operations (LLMOps) experience running LLMs and agents at scale. A track record of full-stack AI delivery, reaching for an AI tool first as a generalist builder. Domain fluency and contextual judgment to validate AI outputs against real business context. A strong business-outcome orientation, owning what the pod builds permanently in production. What We Offer A competitive salary based on your qualities and experience NS business card to cover your commute expenses 25 days of paid holiday per year A laptop and a smartphone A pension scheme Health insurance Organisation driven by technology – we have a tremendous technology backbone Open, 'can do' team spirit and international environment We're excited to meet people who share our mission and can make an impact in a variety of ways. Don't hesitate to apply, even if you only meet the minimum requirements listed. Think about your transferable experiences and unique skills that make you stand out as someone who can bring new and exciting things to this role.

Core Responsibilities

Build and deploy multi-agent AI systems, RAG architectures, and AI-enabled applications that address real business needs. Operate and improve production LLMs and agents, including validating AI-generated outputs and implementing responsible AI guardrails and observability.

Requirements

Requires 3–10 years of technical delivery experience and hands-on expertise with agentic coding, multi-agent frameworks, prompt and intent engineering, and RAG pipelines with vector databases. Candidates should also have experience with LLMOps, model lifecycle management, guardrails, observability, and CI/CD for AI systems.

Benefits

• Competitive Salary
• Commuting Expenses
• 25 Days of Paid Holiday
• Laptop
• Smartphone
• Pension Scheme
• Health Insurance