AI Engineer

About the role

We are seeking an AI Engineer to design, build, and operate production-grade AI and Generative AI solutions across the company. This is a hands-on engineering role focused on transforming architecture and business requirements into working, scalable systems, including LLM applications, AI agents, RAG pipelines, and integrations with our enterprise platforms. The role places strong emphasis on Microsoft Azure and the Microsoft Copilot ecosystem, which serve as our primary enterprise platforms. You will own the full lifecycle of AI features from initial discovery through production deployment and ongoing optimization in a fast-paced environment. You will translate ambiguous business problems into concrete technical designs and then implement robust, testable solutions that directly impact end users. You will act as a technical lead, collaborating closely with product managers and architects to ensure AI capabilities align with strategic objectives. You will champion engineering excellence by establishing patterns and tools that enable other teams to build with AI safely and efficiently.

Key facts

What you'll do

Design and implement AI-driven solutions that automate business processes, reduce manual effort, and improve service delivery across the organization.

Build production-grade GenAI applications

LLM-powered features, AI agents, copilots, and orchestration layers - translating architectural patterns into working code.

Contribute to PoCs and pilots, rapidly prototyping new capabilities and bringing them to a production-ready state when validated.

Develop and integrate AI agents and multi-agent systems using established orchestration frameworks.

Build APIs and microservices that expose AI capabilities to internal teams and products, following architecture and security standards.

Integrate AI solutions with enterprise platforms such as CRM, ERP, ITSM, data warehouse, identity, and observability stack via APIs, events, and connectors.

Ensure designs support scale, reliability, and change management through versioning, backward compatibility, and clear contracts.

Contribute to responsible AI practices and internal policy development by translating principles into engineering standards, reusable patterns, and reference implementations.

Implement governance and security controls in code, including data classification and PII handling, prompt injection defenses and content filtering, audit logging and traceability, and RBAC/least-privilege access patterns.

Apply strong systems thinking to balance innovation with operational stability, ensuring AI features degrade gracefully under failure conditions.

Leverage cloud-native services to minimize infrastructure overhead and maximize experimentation speed for new AI capabilities.

Partner with data scientists to optimize model performance, latency, and cost while maintaining strict compliance and quality standards.

Drive adoption of AI tools within the organization by creating clear documentation, sample code, and internal enablement sessions.

Continuously evaluate new AI technologies and frameworks, proposing experiments that could deliver measurable business value.

Own end-to-end delivery, including requirements refinement, solution design, implementation, testing, and stakeholder communication.

Requirements

Minimum 4+ years of professional software engineering experience, with 1-2 years building and shipping AI/ML or Generative AI solutions in production.

Proven track record of delivering AI features that run in production with real users.

Hands-on experience in enterprise environments, including security, compliance, integrations, and scale.

LLMs & GenAI: deep practical experience with foundation models, prompt engineering, tool/function calling, structured outputs, and context management.

Agents & orchestration: hands-on experience building agents, copilots, or multi-agent workflows.

Microsoft Azure: deploying and operating AI workloads on Azure - specifically Azure OpenAI Service, Azure AI Foundry, Azure AI Search, and related services.

Microsoft Copilot ecosystem: extending or integrating Microsoft 365 Copilot; building custom copilots and agents with Microsoft Copilot Studio.

Python: strong production-quality Python - typing, testing, and async patterns.

APIs & services: REST and/or gRPC; event-driven patterns; API design and versioning.

MLOps fundamentals: experiment tracking, model and prompt versioning, automated evaluation, and monitoring of AI systems in production.

Multi-model experience: familiarity with working across LLM providers and adapting to provider-specific patterns such as tool use, structured outputs, and long-context handling.

Experience with embedded/on-platform AI tooling such as ServiceNow Now Assist or similar ITSM AI capabilities.

Strong communication skills - able to explain technical decisions to both engineers and non-technical stakeholders.

Pragmatic, delivery-oriented mindset; comfortable balancing speed of iteration with production quality.

Fluency in English, written and spoken. Please send your CV in English only.