AI Engineer

Summary

Join Unit4's AI research team as a forward-thinking AI Engineer, playing a pivotal role in shaping the future of enterprise software. Design, develop, and deploy AI systems, intelligent agents, and orchestration frameworks. Focus on streamlining AI solution deployment, monitoring, and continuous improvement using MLOps and LLMOps. Your work will directly influence the evolution of enterprise software into an AI-native platform, enabling intelligent agents to reason, act, and collaborate. The primary responsibility is AI research supporting intelligent automation and orchestration within enterprise systems. Advance current and future AI engineering initiatives across multiple teams and projects.

Requirements

• GenAI Foundations: Proficiency in leveraging Language Models (e.g., LLMs, SLMs), Retrieval-Augmented Generation (RAG), and prompt engineering
• Infrastructure & Deployment: Experience with design and implementation of MLOps pipelines at scale. Familiarity with cloud platforms (Azure, AWS, GCP) and DevOps practices
• Agentic Architecture & Orchestration: Experience in agent development frameworks such as Semantic Kernel, including AI integration patterns and standards such as MCP or A2A
• Data Integration & Event-Driven Systems: Familiarity with event-driven systems like Kafka, Flink, and Spark Streaming
• Programming: Strong programming skills in Python, .NET, and SQL/NoSQL databases
• Collaboration & Communication : Work effectively across architecture, AI, product, and engineering teams
• Agile Mindset : Comfortable with iterative delivery, version control, and DevOps practices
• Governance Awareness : Understanding of data privacy, security, and compliance (e.g., GDPR)
• Strong system thinking and ability to model complex workflows
• Research-oriented mindset with adaptability to emerging technologies
• Curiosity and continuous learning in AI, GenAI, and agentic systems
• Ethical awareness in AI deployment and data governance
• Proactive and self-driven with a passion for innovation

Responsibilities

• Design and implement GenAI solutions using LLMs and RAG capabilities such as Search and Retrieval or GraphRAG
• Define and enforce best practices for prompt templates, advanced prompting engineering, and context-aware prompting
• Contribute to the evolution of the Unit4 AI Foundation platform
• Collaborate on the design and implementation of LLM evaluation frameworks, benchmarks, and performance metrics
• Build modular, task-specific agents using frameworks like Semantic Kernel or similar
• Define best practices for agent tooling on the Unit4 ecosystem using existing standards such as Model Context Protocol (MCP)
• Implement agent orchestration protocols (e.g., A2A) for multi-agent collaboration
• Develop adaptive planning engines aligned with user workflows and business processes
• Collaborate on the design and implementation of agent evaluation frameworks, benchmarks, and performance metrics
• Design cloud-native, scalable infrastructure for agent workloads (Azure, AWS, GCP)
• Build robust pipelines for AI deployment and lifecycle management—combining MLOps and LLMOps practices with CI/CD and Infrastructure as Code (e.g., Terraform, Bicep)
• Ensure observability, monitoring, and feedback loops for agent performance and reliability
• Collaborate with data engineers and architects to integrate structured and unstructured data sources
• Leverage event-driven architectures (e.g., Kafka, Flink) for real-time agent actions
• Define data contracts and schemas to support agentic decision-making
• Work closely with architects, data scientists, data engineers, and product leads to align AI foundational architecture with business goals
• Participate in design and architectural reviews, roadmap planning, and research cross-functional discussions
• Mentor junior engineers and contribute to knowledge sharing across teams

Preferred Qualifications

• 3+ years in software engineering with a strong foundation in modern engineering practices
• Experience in enterprise software, ERP systems, and process automation
• Hands-on experience with GenAI concepts such as Retrieval-Augmented Generation (RAG), Model Context Protocol (MCP), or agent orchestration (A2A)
• Experience in AI/ML lifecycle management (AI operationalization) and governance
• Understanding of data readiness and quality requirements for AI and Agentic systems

Benefits

• A culture built on trust - giving you the freedom and autonomy to be successful
• Balance - with our uncapped time off policy, remote working opportunities and Global Wellbeing Days when the whole company can switch off and prioritize well-being
• Talented colleagues, role models and mentors - work, learn and be inspired by some of the best talent in the software industry
• A commitment to sustainability - with initiatives such as our Act4Good program, a way for everyone at Unit4 to come together and engage in actions that benefit society and the planet
• A safe and inclusive working environment – supported by our Employee Resource Groups, which are open to all and include Women at Unit4, Pride at Unit4, Mental Health and Access at Unit4, and People of Color at Unit4