The Opportunity

We are looking for a hands-on AI Engineer who can turn business problems into secure, reliable and scalable digital solutions. You will design, build, ship and continuously improve AI-enabled products that integrate with enterprise data, APIs and operational systems.

This role suits a practical builder with the right mindset and attitude: curious, accountable, collaborative and comfortable owning delivery from early discovery through production. You will work closely with business, product, data and technology colleagues, while helping strengthen the engineering practices of a growing AI team.

Your Role

• Build and ship production solutions. Develop AI-enabled applications and services from prototype to production, with ownership of quality, reliability and ongoing improvement.
• Design applied AI architectures. Build LLM applications, AI agents, retrieval-augmented generation services, document-intelligence workflows and API-based integrations using fit-for-purpose patterns.
• Develop robust backend services. Write production-grade Python, design scalable APIs and microservices, and implement asynchronous processing for long-running or high-volume workloads.
• Integrate the digital stack. Connect models and AI services with enterprise platforms, data sources, workflow systems, user interfaces and upstream or downstream applications.
• Engineer for trust. Implement automated testing, evaluation, monitoring, guardrails, security controls, human review and fallback paths to manage accuracy and failure modes.
• Operate what you build. Monitor performance, reliability, latency and cost troubleshoot issues and improve solutions after deployment.
• Strengthen delivery practices. Contribute to code reviews, CI/CD, infrastructure-as-code, technical documentation and reusable engineering patterns.
• Collaborate and uplift the team. Explain technical trade-offs clearly, support less-experienced engineers and work constructively across business and technology teams.

Your Skills and Experience

• 5-8 years of hands-on software, data or AI engineering experience, including demonstrable delivery of digital products or platforms into production.
• Strong Python engineering skills and experience developing maintainable backend services, APIs and microservices.
• Hands-on experience with FastAPI or a comparable Python API framework.
• Practical experience delivering GenAI or LLM solutions, such as RAG pipelines, AI agents, document intelligence or workflow automation.
• Experience with orchestration frameworks such as LangGraph, LangChain, LlamaIndex or equivalent.
• Understanding of retrieval design, embeddings, vector databases and search quality, including chunking, metadata, hybrid search or reranking.
• Experience with asynchronous processing and queuing patterns using tools such as Azure Service Bus, Celery, Redis or equivalent.
• Experience with cloud-native delivery on Azure or AWS, containers, CI/CD and infrastructure-as-code.
• Working knowledge of SQL database, application monitoring, automated testing and version control.
• Understanding of privacy, security, data governance and responsible AI considerations in an enterprise environment.
• Strong problem-solving and communication skills, with the ability to translate business needs and architectural trade-offs into practical delivery decisions.

The Mindset We Value

• Builder mentality. You prefer working software and measurable outcomes over purely conceptual designs.
• Ownership. You follow through from problem definition to deployment, adoption and continuous improvement.
• Pragmatism. You select the simplest fit-for-purpose approach and know when conventional software or human review is more appropriate than AI.
• Learning agility. You keep pace with a rapidly evolving AI landscape without chasing tools for their own sake.
• Collaboration. You seek feedback, communicate openly and help the wider team succeed.
• Resilience. You are comfortable navigating ambiguity, production issues and changing priorities with a constructive attitude.

Why Join Us

You will have meaningful ownership of applied AI solutions and the opportunity to shape how they are engineered and operated in a complex enterprise environment. The role combines hands-on building, real business problems and close collaboration with a developing team that is moving from experimentation towards production-grade delivery.