Frontier Engineer

Cognizant; Oslo; Hybrid; NOK 1,800,000 - 2,400,000; Full time

Job summary

Cognizant is seeking a Frontier Engineer to join a lean, high-impact pod. You will design and deploy AI-native systems, partner with clients, and continuously improve AI solutions. The role emphasizes human judgment, safe prompts, and scalable AI architectures in a hybrid setup in Norway.

You will work with LangChain, AutoGen, and LangGraph, building end-to-end AI systems and ensuring production readiness with guardrails and observability.

Qualifications

• 3-10 years of technical delivery experience with AI fluency across the full stack.
• Hands-on experience with agentic coding and multi-agent orchestration.
• Strong prompt and intent engineering skills to validate AI-generated code.
• Experience designing RAG pipelines and managing vector databases.
• LLMOps and model lifecycle management including guardrails and observability.

Responsibilities

• Build and orchestrate multi-agent AI systems for production environments.
• Design prompts and workflows for AI coding agents with quality and safety checks.
• Create scalable RAG architectures and knowledge systems for context-aware AI.
• Deploy, monitor, and optimize LLMs and agents with observability practices.
• Develop AI-embedded applications using modern engineering practices.

Skills

LangChain

AutoGen

LangGraph

Prompt engineering

LLMOps

Vector databases

CI/CD for AI

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 Norway. 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.