JOB  SUMMARY

What you’ll do

• Build internal AI tools that support consulting workflows, including evidence synthesis, proposal review, knowledge retrieval, quality assurance and document production.
• Develop and maintain data pipelines that ensure consultants have cutting-edge data available for their projects.
• Develop connectors between AI and other systems to enable more effective workflows
• Prototype new AI applications with consulting teams, then use feedback and measurement to decide what should be stopped, improved or scaled.
• Maintain production AI tools, including monitoring, bug fixes, user support and iterative improvement.
• Integrate LLMs, retrieval systems, APIs, databases, and internal knowledge sources into secure, usable applications.
• Contribute to technical choices for Nous’ product portfolio, including build-versus-buy decisions, architecture, deployment patterns, and evaluation methods.
• Work with other Nous team members and consulting teams so tools fit Nous’ delivery standards, security requirements, and operating model.

What will help you succeed

• AI and data engineering experience, including hands-on delivery of working applications.
• Practical experience building with LLMs in production settings.
• Experience working with or developing AI connectors, preferably connectors that retrieve information from complex systems
• Demonstrated capability developing data pipelines between systems, preferably using Microsoft Fabric and/or Data Build Tool (DBT)
• Experience with LLM application patterns such as prompting, retrieval-augmented generation, embeddings, vector databases, tool use, function calling, and evaluation.
• Agentic engineering capability: able to use coding agents such as Claude Code, Codex, or similar tools to accelerate software delivery, including task scoping, prompt design, code review, testing, debugging, and integrating agent-generated changes into a maintainable codebase.
• Experience designing and building APIs and MCPs.
• Good engineering habits: version control, testing, code review, documentation, logging, and secure handling of data.
• Evidence of practical AI build work. This may include internal tools, side projects, open-source work, demos, prototypes, or shipped products.
• High agency. The AITT will be small, so the role needs someone who can take an unclear problem, shape it with users, build a working solution, and improve it after release.

You may also bring

• Experience with the Azure OpenAI API or AWS Bedrock ecosystems.
• Experience with cloud deployment, preferably Azure.
• Experience with CI/CD pipelines and infrastructure-as-code.
• Experience with Microsoft Fabric and Data Build Tool (DBT)
• Background in management consulting, professional services, or another environment where written judgement and client delivery standards matter.
• Experience building tools for knowledge work, document-heavy workflows, research, analysis, or decision support.

Are you interested in this position?

Apply by clicking on the “Apply Now” button below!

#DesignFintech #GlobalDesigners
#FintechInnovation #CreativeJobs
#DesignHub
#Tech Meets Design
#DesignerNetwork
#Myausjob