1001ai — Applied AI Engineer (Senior)
Category: Machine Learning | Priority: P0 | Headcount: 1
About the role
Bridges the gap between deep ML and full-stack engineering. Takes ML prototypes and ships them as production-grade AI features inside our enterprise deployments.
1001ai is an early-stage startup (~12 people, mostly engineers) building "Scale AI meets Palantir"-style capabilities for heavy-infrastructure clients across aviation, sports, construction, and defense. The core engineering team is largely ex-Scale AI. Roles are based across London and the GCC (Qatar, UAE), with Qatar as the priority location; exceptional candidates can flex on location but should be open to international travel.
Responsibilities
- Take ML prototypes and make them production-ready - APIs, services, evaluation harnesses, monitoring.
- Turn ambiguous product asks into working AI workflows, not just wrapping models with APIs.
- Build sophisticated AI features from product requirements without needing to do the underlying ML research.
- Work across multiple live enterprise deployments as part of the core engineering team.
- Own end-to-end systems from model code through to product surface.
Requirements
- 6+ years as a software engineer, with at least 2 of those years deploying and maintaining ML models in production.
- Engineering-first profile - not a research profile, but ML-literate.
- Strong fundamentals: distributed systems, APIs, data pipelines, testing.
- Hands-on with modern LLM and agent tooling (frameworks like LangChain / LlamaIndex / DSPy or equivalent, vector stores, eval/observability tooling).
- Production TypeScript and Python.
- Track record of shipping ML-backed features at a top-tier tech company, AI lab, or fast-moving startup.
- Top CS / engineering degree or equivalent demonstrated experience.
As a senior role this includes mentorship - not just individual contribution.
What we value across all engineering hires
- Comfortable in a high-ownership early-stage environment where scope is broad and changes quickly.
- Ships production systems; prior enterprise deployment experience is a major plus.
- Strong engineering judgement over knowledge of one exact tool or framework.
- Pragmatic tradeoffs between speed, quality, security, and maintainability.
- Uses AI coding agents effectively to accelerate implementation but remains fully accountable for code quality, architecture, security, and maintainability.
- Comfortable with hybrid / on-prem / sovereign cloud enterprise deployments.
- A high-performance, "work hard" culture - intense but not toxic.