Artificial Intelligence Engineer

Applied AI, Machine Learning Engineer Location | London (Hybrid) Comp | Highly Competitive + Equity Applied AI / Machine Learning Engineer — Singapore Focus | Applied AI, Machine Learning, GenAI, Production ML Systems, End-to-End Deployment We’re partnering with one of the most ambitious AI companies globally, building and deploying advanced AI systems to solve complex, high-impact problems for major organisations across the private and public sectors. Backed by some of the most respected names in technology and venture capital, the company brings together engineers, researchers and operators from leading AI and technology organisations to build AI systems that move beyond demos and into real-world production. Following significant growth internationally, they are now expanding their engineering team in Singapore and are looking for exceptional AI / ML Engineers who can take complex problems, develop the right machine learning approach and turn that into scalable, production-ready systems. This is an opportunity to work across the full AI lifecycle from problem definition and modelling through experimentation, engineering, deployment and iteration — while seeing your work directly influence large-scale organisations and real-world outcomes. About The Role As an Applied AI / Machine Learning Engineer, you’ll work on difficult, often ambiguous problems where machine learning and AI can materially improve the way organisations operate. Rather than working on isolated models or narrow research problems, you’ll own problems end-to-end: understanding the underlying challenge, determining the appropriate modelling approach, building and evaluating solutions, and deploying them into production environments. The work can span classical machine learning, deep learning, generative AI and LLM-based systems depending on the problem being solved. You’ll work closely with other engineers, technical leaders and end users to ensure the systems you build are technically strong, practical and capable of delivering measurable impact. Examples Of The Types Of Problems You Might Tackle Building ML systems that predict, classify, optimise or automate complex operational decisionsDeveloping intelligent workflows combining structured data, unstructured data and language modelsDesigning and evaluating models for high-stakes real-world environmentsBuilding LLM and agent-based systems where generative AI is the right technical solutionDeveloping data pipelines, APIs and supporting infrastructure required to productionise AI systemsImproving model accuracy, reliability, latency and scalability in productionTaking an ambiguous business or operational problem and translating it into a well-defined machine learning problemBuilding systems that continuously improve as new data and feedback become available What You’ll Do Build production AI systems Design, develop and deploy machine learning and AI systems that solve complex real-world problems. Own the full ML lifecycle Work across problem formulation, data exploration, feature engineering, modelling, experimentation, evaluation, deployment and monitoring. Choose the right technical approach Apply classical ML, deep learning, LLMs or hybrid approaches depending on what the problem actually requires rather than defaulting to a single technology. Engineer for production Build reliable software, data pipelines, APIs and infrastructure around your models so solutions can operate effectively at scale. Work closely with end users Understand how organisations operate, identify where AI can create genuine leverage and rapidly iterate based on real-world feedback. Solve ambiguous problems Turn loosely defined, high-level challenges into measurable technical problems and practical solutions. Shape the engineering culture Contribute to technical decisions, engineering standards and the way AI systems are built and deployed across the organisation. What We’re Looking For Strong professional experience across Machine Learning, Applied AI or ML EngineeringExcellent coding ability, particularly in PythonStrong software engineering fundamentals and experience building production systemsStrong understanding of core machine learning conceptsExperience taking machine learning models from experimentation into productionAbility to work comfortably with complex or imperfect real-world datasetsStrong understanding of system design and the engineering required around ML systemsExperience independently owning technically challenging projectsStrong communication skills and the ability to explain technical decisions clearly You’ll Likely Thrive Here If You enjoy solving difficult problems rather than simply implementing predefined requirementsExperience building applications using LLMs, foundation models or generative AIYou’re equally comfortable thinking about models, data and software engineeringYou care about whether an AI system actually works in production, not just whether the model performs well in a notebookYou naturally think about evaluation, failure modes and how to improve systems over timeYou’re comfortable operating with ambiguity and significant ownershipYou like working directly with the people ultimately using the systems you buildYou want to see a clear connection between your engineering work and tangible real-world outcomesYou’re excited by both traditional machine learning and the opportunities emerging from generative AI Nice To Have Experience with RAG, agents, tool use or structured LLM workflowsExperience fine-tuning or adapting open-source modelsExperience with deep learning frameworks such as PyTorchExperience designing ML evaluation frameworks and experimentation systemsExperience with cloud infrastructure and production ML platformsExperience working in high-growth technology companies, research environments or technically demanding engineering teamsStrong academic background in Computer Science, Machine Learning, Mathematics, Statistics or a related discipline What’s On Offer Highly competitive compensationMeaningful equity upsideOpportunity to work alongside exceptional AI engineers, researchers and operatorsSignificant technical ownership from day oneExposure to some of the most interesting and consequential applied AI problems being tackled globallyThe ability to see systems you build deployed into real organisations and create measurable impactA fast-moving environment where strong engineers can have outsized influence on both the technology and the company If you’re interested in learning more, feel free to reach out | danny@salientgroup.com.au