Senior Software Engineer (AI, AWS & Python)

Here’s a summary of the role:   

You’ll be a Senior Software Engineer helping shape how AI is built, deployed, and scaled across Diligent’s platform. Working at the intersection of Applied Science, Product, and Engineering, you’ll turn machine learning and LLM-based ideas into secure, reliable, production-ready services that accelerate AI adoption across Diligent’s product suite. 

This role is about combining strong software engineering fundamentals with practical AI delivery. You’ll design and build scalable platform services, develop shared libraries and frameworks, improve evaluation and testing practices, and help teams embed AI responsibly into customer-facing products. You’ll work on systems that handle sensitive customer content, so privacy, security, observability, and governance need to be built in from day one. 

If you enjoy solving hard platform problems, writing high quality code in Python and TypeScript, partnering closely with Applied Scientists and Product teams, and using AI as a practical tool to deliver meaningful value, this role is for you. 

Here’s a breakdown of what you’ll do (not all of it, just the important stuff): 

• Design, develop, deploy, monitor, scale, and troubleshoot ML and LLM-based systems that power shared AI services across the platform. 

• Build secure, high performance microservices and platform components on AWS, owning services end-to-end from implementation through deployment and production monitoring. 

• Collaborate with Applied Scientists, Product teams, and other Software Engineers to implement and productize AI-based capabilities. 

• Continuously raise the bar by evaluating and piloting new tools (including latest AI tooling) to improve performance, cost efficiency, and developer productivity. 

• Shape and evolve AI platform infrastructure by applying established organisational patterns and improving them where needed. 

• Design and develop reusable AI libraries, frameworks, and internal tooling that accelerate delivery across multiple teams. 

• Stay ahead of AI and platform trends, adapting designs and practices so your systems remain robust, compliant, and future‑proof. 

These are the essentials you’ll need to get an interview: 

• 5+ years of experience delivering secure, scalable applications in agile environments. 

• Strong ability to write highly readable, maintainable, and well-tested code, with a solid grounding in software design principles. 

• Strong hands-on skills in Python and Node.js/TypeScript on a major cloud platform (preferably AWS), including serverless or microservice patterns, infrastructure as code (CDK/Terraform), CI/CD, containerization, and observability tooling. 

• Experience building and operating backend services or microservices in a major cloud environment, preferably AWS. 

• Good understanding of the full lifecycle of AI-based solutions, from experimentation and implementation through deployment, monitoring, and iteration. 

• Practical experience working with LLM-based systems or adjacent AI technologies. 

• Ability to collaborate effectively across disciplines and help translate applied science work into robust product capabilities. 

• Habit of integrating security best practices (IAM, secrets management, privacy controls) into everything you build — including AI components. 

• Excellent communication and leadership skills: you explain complex technical and AI topics clearly to different audiences. 

It would be great if you had these to, but we’ll support you if you don’t: 

• Hands-on experience with AWS CDK. 

• Hands-on experience with LLM-related techniques such as prompt engineering, fine-tuning, model evaluation, RAG, agentic solutions, or MCP servers. 

• Experience developing AI evaluation, observability, or governance mechanisms for production systems. 

• Experience shaping shared platform capabilities or reusable engineering patterns across multiple teams. 

• Experience improving performance, reliability, or cost efficiency for cloud-based AI or data-intensive services. 

• Interest or experience in mentoring peers, raising engineering standards, and helping teams adopt better ways of working. 

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