Solutions Engineer

As a Solutions Engineer (you'll be known internally as a Forward Deployed Engineer), you are the architect of our customers' technical success. You will lead the deployment and integration of our AI OS into the heart of enterprise infrastructures to deliver tangible business value at scale.

Your primary metric of success is Speed to Value: the velocity at which you can move from initial engagement to robust, functional enterprise AI deployment at scale.

Your role

Key Responsibilities

• Accelerate Deployment Velocity: Lead the end-to-end technical delivery of our AI Operating System for customers, prioritising the fastest path from initial data ingestion to production-grade deployment.
• Bridge the Integration Gap: Design and build robust connections between our distributed AI architecture and the customer’s existing data stack, legacy systems, and cloud infrastructure.
• Solution Architecture: Act as a hands-on engineer and consultant, rapidly developing functional prototypes and workflows that demonstrate the power of our AI OS against specific business use cases.
• System Hardening: Work with the engineering teams to transition successful prototypes into resilient, scalable solutions by applying high engineering standards to high-velocity deployments.
• Feedback Loop: Partner with our Product Engineering and R&D teams to turn field-level challenges into core platform features, reducing friction for all future deployments.

The Speed to Value Mindset

• Technical Communication: You can translate complex distributed systems concepts into clear, actionable roadmaps for both technical and non-technical stakeholders.
• Pragmatic Delivery: Through a business outcome focus, you identify the 20% of engineering work that delivers 80% of the customer value. You prefer functional, reliable solutions that deliver value over theoretical perfection.
• Ownership & Agency: You take full responsibility for the customer's technical success. You identify and build solutions to keep the deployment moving.

• Core Engineering: Proficiency in Python. You are comfortable writing, debugging, and optimising code.
• AI Assisted coding: You have hands of experience using AI to accelerate software development and can confidently manage the risks while realising benefits in cost and speed.
• AI & Logic Implementation: Practical experience with AI orchestration (e.g. RAG architectures, knowledge graphs, agentic frameworks, or LLM-based reasoning systems).
• Data Infrastructure: Solid grasp of SQL and experience navigating enterprise data pipelines (e.g. Snowflake, Databricks). You understand how to handle "messy" real-world data at scale.
• DevOps & Cloud: Familiarity with Docker, Kubernetes, and at least one major cloud provider (e.g. AWS, Azure, or GCP).
• Distributed Systems: An interest in or experience with high-performance networking, disaggregated computing, or decentralised workloads.

At Stelia, we are building the AI Operating System for a distributed, intelligent world. Our mission is to dismantle the boundaries between humanity and technology by creating an Enterprise AI designed for trust, resilience, and scale.