Senior AI Engineer - LLMs & Agentic Systems (all genders)
Role details
Job location
Tech stack
Job description
The HRS AIOps team serves as a pivotal force in transforming customer support by harnessing the power of advanced AI technologies. Our primary goal is to enhance customer satisfaction by providing swift, intelligent solutions to inquiries, ensuring a seamless experience across all channels. By focusing on proactive support and personalised interactions, we empower customers to resolve issues autonomously, significantly reducing response times and minimising frustration. Through continuous improvement and real-time insights, the AIOps team plays a crucial role in elevating service quality, allowing our support agents to concentrate on complex tasks that drive operational excellence and foster innovation., We are seeking a Lead AI Engineer to drive the development and deployment of agentic AI capabilities across our travel technology platform. This is a hands-on engineering role focused on building production-grade AI agents that can plan, reason, retrieve information, and execute complex travel workflows at scale.
You will architect multi-agent systems, integrate LLMs with internal and external services, and ensure these agents operate with reliability, safety, and real-world performance. The Lead AI Engineer will collaborate with Product Managers, Designers, and engineering teams to deliver solutions that enhance customer experience and operational efficiency., 2 .Lead the technical implementation of AI agent frameworks while maintaining cohesion with product vision
- Drive innovation in agentic AI capabilities including multi-agent orchestration, tool integration, and autonomous decision-making
- Establish observability and feedback loops for continuous agent improvement and learning
Technical Excellence - Agentic AI Focus
- Design and implement scalable AI agent architectures with planning, reasoning, and execution capabilities
- Develop multi-agent systems for complex travel workflows (booking, itinerary planning, customer support, operations)
- Build robust agent orchestration platforms with tool integration and API management
- Implement agent memory systems, context management, and learning mechanisms
- Ensure agentic systems meet performance, reliability, safety, and controllability requirements 6 .Establish comprehensive monitoring, logging, and debugging capabilities for agent behaviors
AI Engineering & PSDLC Leadership
- Drive rapid prototyping and iterative development cycles for agent capabilities
- Implement strong feedback loops and A/B testing frameworks for agent performance optimization
- Establish MLOps/AIOps pipelines specifically designed for agent deployment and monitoring
- Lead prompt engineering, agent fine-tuning, and behavioral optimization initiatives
- Ensure quality gates, safety measures, and guardrails are built into agent systems
Team Leadership
- Manage and mentor Data Scientists and AI engineers specializing in agentic systems
- Foster a culture of experimentation, rapid learning, and technical excellence
- Drive adoption of agentic AI best practices, safety protocols, and development standards
- Balance innovation velocity with system reliability and user trust
Cross-functional Collaboration
- Partner with Product Managers to translate business requirements into agent capabilities
- Work closely with Process and Product Designers to ensure agent interactions enhance user experience
- Coordinate with platform teams for seamless integration of agent services
- Facilitate communication about agent capabilities and limitations to stakeholders, Access to a global network of a globally united and mutually responsible "Tribe of Intrapreneurs" that is passionately dedicated to renew the travel industry and while doing so reinvent the ways how businesses stay, work and pay.
Our entrepreneurial driven environment of full ownership and execution focus offers you the playground to contribute to a greater mission, while growing personally and professionally throughout this unique journey. You will continuously learn from a radical culture of retrospectives and continuous improvement and actively contribute to making business life better, smarter and more sustainable.
Requirements
- Bachelor's or Master's degree in Computer Science, Engineering, or related field, or equivalent practical experience building and shipping AI/ML systems.
- 6+ years of professional software engineering experience, ideally across backend, distributed systems, or platform engineering.
- 1-3 years of hands-on experience building production-grade AI/ML or LLM-driven systems (beyond prototypes or notebooks).
- Demonstrated ability to architect, implement, and operate large-scale, high-reliability systems in cloud environments.
- Experience working in fast-paced, iterative product environments with rapidly evolving AI technologies.
Agentic AI Technical Expertise
- Strong experience building multi-agent systems using LangGraph, CrewAI, Haystack workflows, AutoGen, or custom orchestration frameworks.
- Deep expertise in LLM integration, prompt engineering, evaluation frameworks, safety controls, and hybrid model strategies.
- Proven capability designing tool-calling workflows, external API integrations, and complex reasoning/planning loops.
- Experience with retrieval systems, embedding optimization, vector store design, and episodic memory architectures.
- Solid understanding of agent planning algorithms, reasoning frameworks, and decision-making systems.
AI Engineering & Platform Skills
- Strong Python engineering skills plus experience with distributed systems, async patterns, API development, CI/CD, and containerization.
- Demonstrated ability to build production-scale AI systems with performance tuning, testing frameworks, observability, reliability, and safety gates.
- Hands-on experience with the AWS AI/ML stack: SageMaker, Bedrock, Lambda, Step Functions, API Gateway.
- Familiarity with AWS data and search services: DynamoDB, Aurora, OpenSearch, S3, Glue, Athena.
- Cloud infrastructure and operations experience: ECS/EKS, CloudWatch, EventBridge, IaC (CDK/Terraform).
- Comfort with open-source tooling: Kubernetes, Ray, MLflow, Prefect/Temporal.
- Experience running, tuning, or quantizing open LLMs (LLaMA, Mistral, etc.).
- Familiarity with open-source RAG stacks (LlamaIndex, LangChain OSS, Haystack) and OSS observability tools.
Benefits & conditions
The attractive remuneration is in line with the market and, in addition to a fixed monthly salary, all necessary work equipment and mobility, will also include an annual or multi-year bonus. #LI-AS1 Req ID: 18183
