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.