Lead AI Back End Software Engineer (AI Architect Track)

Description

We are looking for a Senior/Lead AI Back End Software Engineer (AI Architect Track) who will drive the architecture and delivery of AI-powered Search solutions, taking ownership of end-to-end system design, model integration, and platform scalability while guiding teams toward production-ready, high-quality outcomes.

About Nortal: 

We’re Nortal. We think big and create cutting-edge digital solutions with a global reach. And with 25 years of experience, 2,700+ professional experts, and half a billion people worldwide impacted by our work, we believe we’ve got the numbers to back up that statement.  

Our global teams have played a significant role in many Fortune 500 companies’ projects and systems and have been the driving force of digital transformation for governments, healthcare institutions, and leading enterprises worldwide. We combine best-in-class strategic consulting with software engineering, data, and design practices to bring our visions to life. 

About the project
TUI, is a global business with over 70 000 people on board, great history and challenging plans for building a digital future. TUI is the largest leisure, travel and tourism company globally, and it owns travel agencies, hotels, airlines, cruise ships and retail shops.

You will join a global Search Services team within a large-scale digital platform, responsible for filtering, sorting, and enriching product data for customer-facing systems. The team operates in a highly distributed environment across multiple countries and focuses on building AI-powered search capabilities that enhance user experience through faster, smarter, and more relevant results.

Responsibilities

• Lead the architecture and design of AI-powered Search solutions, covering services, data flows, and model integration

• Drive end-to-end delivery from discovery and validation to production and continuous improvement

• Design and implement solutions using GenAI models, including orchestration, evaluation strategies, and retrieval-augmented generation (RAG)

• Define reference architectures and best practices for secure, scalable, and cost-efficient model usage

• Ensure platform reliability, including latency optimisation, observability, security, and cost control

• Conduct and guide proof-of-concepts (POCs), validating approaches based on performance, accuracy, and cost

• Collaborate closely with architecture, product, data, and engineering teams to align solutions and trade-offs

• Lead technical discussions, workshops, and design reviews across teams

• Mentor engineers and contribute to building a strong engineering culture

• Research and promote new technologies, tools, and approaches in AI and backend engineering

Qualifications

• Strong experience designing and operating large-scale backend systems in production

• Deep proficiency in Java or Kotlin, with hands-on experience in cloud-native development

• Strong experience with microservices architecture, including performance tuning and reliability patterns

• Experience with AWS cloud architecture (networking, IAM, observability, scalability)

• Practical experience delivering AI/LLM-based solutions in production (e.g. RAG, orchestration, evaluation, monitoring)

• Strong understanding of ML/AI concepts (embeddings, retrieval, model evaluation, safety considerations)

• Experience with Python and AI/ML tooling

• Experience with REST and/or GraphQL APIs

• Experience working with SQL and/or NoSQL databases

• Strong DevSecOps mindset and experience with CI/CD pipelines

• Excellent communication skills and ability to work with both technical and non-technical stakeholders

• Leadership mindset with experience mentoring or coaching engineers

• High level of English (written and spoken)

• Proactive, ownership-driven, and comfortable working in ambiguous environments

Nice to Have

• Hands-on experience with AWS Bedrock and GenAI tooling

• Experience with OpenSearch / Elasticsearch and search relevance tuning

• Experience with Terraform and Infrastructure as Code

• Familiarity with GitLab CI or similar CI/CD tools

• Experience defining LLM evaluation frameworks (prompt versioning, testing, red-teaming)

• Understanding of event-driven architectures

• Experience working in global, distributed teams

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