Sr. Forward Deployed Engineer (Office of the CEO)

About the Forward Deployed Engineer (Office of the CEO) role

This is a high-impact, highly autonomous builder role embedded within the Office of the CEO.

Reporting directly to the Office of the CEO, you will be the inaugural Forward Deployed Engineer, focused on critical initiatives. In this role, you will operate at the intersection of executive strategy, technical execution, and start-up building. You will work closely with the CEO and senior leadership to identify high-leverage problems across the firm, and rapidly design, build, and deploy solutions that drive measurable impact.

This is not a traditional engineering role. You are not maintaining a single system or working from a fixed roadmap. Instead, you will move across teams and problem spaces, acting as a “startup builder” inside the firm, owning problems end-to-end from discovery through production. This role is an opportunity to build a startup-like capability within Keystone, with direct exposure to executive decision-making and firm-wide impact. You will work on the highest-priority problems, see your solutions adopted quickly, and help define how modern AI and software reshape how the firm operates.

This is not an ordinary role. We are looking for an incredibly high-agency builder who is energized by a start-up culture, thrives in ambiguity, bridges the gap between strategic vision and technical reality, and fundamentally loves to learn and ship at the frontier of technology innovation.

The role will also work alongside our Keystone Artificial Intelligence & Machine Learning (K.AIML) team, focused on building, deploying, and scaling AI-powered tools that enhance Keystone’s impact.

Key Responsibilities

• Problem Discovery & Execution: Embed with teams to understand how work gets done, identify high-leverage inefficiencies, and translate them into concrete technical solutions
• AI Integration: Design and deploy practical, production-ready applications using LLMs, RAG pipelines, and modern AI tooling
• Agentic Workflows: Build and implement AI-driven workflows that automate repetitive or time-intensive tasks, using emerging frameworks where appropriate

• Data Integration: Work with fragmented internal and external datasets to build reliable, automated data pipelines and systems
• Dashboarding: Create real-time, decision-grade dashboards and tools for the CEO and leadership team
• Full-Stack Development: Rapidly prototype and ship internal applications (from backend logic to simple frontends) with a bias toward speed and usability
• AI-Assisted Development: Leverage modern AI-assisted development tools to move quickly while maintaining strong standards for code quality, security, and scalability

• Applied Modelling: Develop and implement forecasting or predictive models tied to real business use cases (e.g., operational bottlenecks, pipeline trends, or market dynamics)
• Analytical Problem Solving: Apply a rigorous, data-driven approach to ambiguous problems, using modern data tooling and statistical techniques
• End-to-End Ownership: Own the full lifecycle of systems you build—scoping, prototyping, validation with stakeholders, and production deployment

What You’ll Bring

• Builder’s Mindset: Demonstrated track record of shipping real products, tools, or systems—not just prototypes
• Education: Bachelor’s degree in a highly technical field such as Computer Science, Mathematics, Software Engineering, Physics, or Data Science
• Experience: ~4–6 years of professional experience in software engineering, data engineering, or a similarly fast-paced technical building role, including product management.
• Technical Skills: Strong proficiency in Python and/or TypeScript, with solid foundations in APIs, data modelling, and relational databases
• Communication: Ability to clearly explain technical concepts and trade-offs to non-technical stakeholders, including senior leadership, and rapidly navigating a matrixed organisational structure

• AI & Data Systems: Experience building with LLMs, RAG architectures, or modern data pipelines
• Product Intuition: Strong judgment on what to build, how to prioritize, and how to iterate quickly based on user feedback
• Autonomy: Ability to operate independently, navigate ambiguity, and make pragmatic trade-offs under pressure