Machine Learning Engineer

What You'll BuildDesign and develop AI agents and agentic workflows powered by large language models (LLMs), combining retrieval-augmented generation (RAG), reasoning frameworks, and tool orchestration.Build intelligent multi-step systems that leverage planning, memory, and external tools to solve complex business and operational challenges.Develop and maintain MCP-based architectures (or equivalent orchestration frameworks) to enable structured context management, tool interoperability, and reliable agent execution.Contribute to AI-driven recommendation, classification, forecasting, and decision-support systems operating on large-scale, real-world datasets.Automate complex workflows and business processes through AI, delivering measurable improvements in efficiency, decision quality, and operational performance.What You'll DoOwn AI initiatives end-to-end, from discovery and experimentation through production deployment, monitoring, and continuous optimisation.Design, build, and deploy production-grade AI agents that operate reliably at scale in real-world environments.Integrate AI capabilities into products, APIs, and business workflows, ensuring solutions are scalable, maintainable, and deliver clear business value.Collaborate closely with software engineers, platform teams, and stakeholders to build robust, observable, and resilient systems.Make pragmatic engineering decisions that balance model quality, latency, reliability, and cost efficiency.Core RequirementsStrong Python engineering skills with the ability to write clean, maintainable, production-quality code and apply sound software design principles.Proven experience deploying LLM-powered applications into production, with demonstrable examples of systems delivering real business value.Hands-on experience building AI agents and agentic workflows, including tool integration, orchestration, planning, and multi-step reasoning.Experience developing and deploying RAG architectures that move beyond proof-of-concept implementations and deliver measurable outcomes.Familiarity with MCP frameworks or equivalent orchestration patterns, including structured context management and tool integration (e.g., FastMCP, FastAPI, LangGraph, LangChain).Strong understanding of LLM capabilities, limitations, and trade-offs, with practical experience mitigating hallucinations, latency, reliability, and cost challenges.Experience deploying and operating systems in cloud environments such as AWS, GCP, or Azure using modern engineering and DevOps practices.Working knowledge of SQL and data manipulation techniques.Ideal ProfileMaster's degree or higher in Computer Science, Mathematics, Engineering, Data Science, Physics, or a related quantitative discipline.Demonstrated experience building, shipping, and iterating on production AI systems, with the ability to clearly articulate architectural and technical decisions.Strong sense of ownership and accountability, with a track record of driving initiatives independently and delivering outcomes.Product-minded approach, focused on solving business problems and creating impact rather than solely optimising model performance.Comfortable operating in fast-paced, ambiguous environments while maintaining high engineering standards.Collaborative team player who contributes positively to team culture, knowledge sharing, and continuous improvement.For Lead-level candidates, experience mentoring engineers and owning complex projects or workstreams from conception through delivery.Strongly PreferredExperience building SaaS, B2B, or enterprise AI products.Background working in high-growth or scaling organisations where speed, execution, and pragmatism are critical.Evidence of production AI systems that are actively used by customers or internal stakeholders and delivering measurable value.Experience designing AI platforms, agent ecosystems, or enterprise automation solutions.Why Join UsBuild AI systems that are live in production and delivering real-world impact at scale.Join a strategic AI programme with strong executive sponsorship, investment, and long-term commitment.Enjoy significant ownership, autonomy, and visibility across both product and business initiatives.Help shape how AI is adopted and operationalised across a global organisation.Work alongside experienced engineers, product leaders, and AI practitioners solving meaningful business challenges.