Machine Learning Engineer

What You'll Build • Design 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 Do • Own 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 Requirements • Strong 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 Profile • Master'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 Preferred • Experience 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 Us • Build 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.