• Design end-to-end AI systems: combining LLMs, embeddings, retrieval pipelines, and structured outputs into reliable product features. • Build grounding and retrieval layers: indexing docs, OpenAPI specs, and customer data to support accurate, context-aware generation. • Define evaluation systems: measuring accuracy, latency, cost, and user impact; building feedback loops to continuously improve quality. • Handle real-world complexity: malformed specs, inconsistent docs, edge-case APIs, and ambiguous user queries. • Make architectural tradeoffs: latency vs. quality, cost vs. coverage, deterministic vs. generative approaches. • Own production reliability: observability, fallbacks, rate limiting, and safe degradation when systems fail. • Work across the stack: from model orchestration and backend systems to the UI surfaces that expose them.

Role Description Are you passionate about leveraging AI to transform global industries? Do you have a proven track record of working with LLMs and building agentic solutions? Join Elixirr Digital as an AI Engineer, where you will drive innovation and help shape the future of intelligent systems for clients across industries like banking, insurance, healthcare, telecommunications, consumer goods, and more. What will you be doing as an AI Engineer at Elixirr Digital? - Design and Implement Solutions: Architect, design, and develop advanced AI systems with a focus on LLMs and agentic solutions that transform enterprise workflows. - Collaborate: Collaborate with cross-functional experts to deliver AI-driven innovations and mentor junior engineers. - Drive Innovation: Work on a mix of internal R&D projects and enterprise-level solutions, building and optimizing tools for both internal and client use. - Deploy at Scale: Develop and maintain production-ready AI solutions, ensuring scalability, performance, and reliability. - End-to-End Lifecycle: Own and contribute to the entire AI development lifecycle—from research and prototyping to deployment and monitoring. - Deliver Tangible Outcomes: Help global enterprises achieve measurable impact through solutions like underwriting agents, airport assistants, pharmacy workflow optimizers, and customer due diligence systems. Qualifications - Hands-on experience working with LLMs, with demonstrated expertise in building and deploying agentic solutions using frameworks such as LangChain, AutoGen, Crew, or Swarm. - Solid background in programming and past experience in machine learning, with a deep understanding of at least one area such as deep learning, reinforcement learning, fine-tuning, or model development. - Ability to design and lead AI solutions while also contributing directly to development. Excellent problem-solving, analytical, and critical thinking skills, with a proficiency in applying mathematical principles to analyze, model, and solve complex problems. - Passion for transforming businesses and delivering measurable results through AI. - Experience working in cross-functional teams, balancing R&D and production-level work. - Stay ahead of AI trends, continuously innovate by building and refining tools for internal and client use, and actively apply cutting-edge research to improve processes and develop scalable, impactful solutions. Requirements - Proven track record of leading AI projects from concept to deployment. - Strong understanding of the AI development lifecycle, with deep expertise in at least one part of the process. - Examples of deployed AI systems demonstrating agentic capabilities. Benefits - Individually tailored benefits package - Performance bonus - Employee Stock Options Grant - Employee Share Purchase Plan (ESPP) - Competitive compensation - Flexible working hours and remote work - Multi-sport card - Full medical checkup - Learning and development budget - Growth and development opportunities - Referral bonus - Anniversary bonus

• Design scalable AI architectures for customer use cases in line with enterprise and security standards • Lead implementation of AI software components such as model serving, orchestration, retrieval, and integrations • Run time-bound proofs of value to demonstrate business outcomes quickly and clearly • Help migrate AI workloads between cloud, on-premises, and hybrid environments on Dell infrastructure, optimizing performance, reliability, and security • Take shared responsibility for production readiness and early production stability, including monitoring, performance optimization, and issue triage • Work closely with AI Data Specialists to define and consume AI-ready data pipelines that support performance and reliability • Collaborate with AI Strategists and Enterprise Architects to align technical scope with business outcomes and target-state architectures • Provide documentation, runbooks, and knowledge transfer to customer teams, and contribute to Dell AI Factory reference architectures and playbooks • Mentor other technical team members on AI architecture patterns, Dell AI infrastructure capabilities, and responsible AI practices

• Design and implement LLM-powered applications, including RAG pipelines, AI agents, chatbots, and generative AI integrations. • Manage AP/AR, invoice processing, collections, and payment runs. • Build, optimize, train, fine-tune, and evaluate machine learning and data science pipelines. • Process payroll in accordance with AU legislation. • Architect, deploy, and maintain AI infrastructure, including containerized environments, CI/CD pipelines for ML, monitoring, and scalable production systems. • Prepare financial and management reports (BS, P&L, Cash Flow). • Integrate with commercial LLM APIs (e.g., OpenAI, Anthropic) and open-source models. • Support AU accounting compliance; BAS/IAS prep is a plus. • Engage with business users and stakeholders to interpret requirements, scope AI use cases, and translate ambiguous needs into clear technical specifications. • Collaborate with product, engineering, and non-technical teams to align AI solutions with business objectives and user workflows. • Establish best practices for prompt engineering, model evaluation, and responsible AI development. • Document system architectures, runbooks, and handover materials to support long-term maintainability.