Lead Software Engineer – Agentic AI, NLP & Production ML | Applied AI Lab
I’m recruiting for a global leader in professional information and enterprise software. Their applied AI lab needs a Lead Engineer who has built agentic and LLM systems that run in production, not just in a notebook.
The opportunity
This is a lab that experiments, builds and delivers. You’ll lead technical direction across multiple initiatives, set engineering standards that scale, and work with research scientists to turn ideas into products used by legal, tax, accounting and government professionals worldwide.
What you’ll do
- Provide technical leadership: set the standards and best practices that help a global team move faster
- Own the full lifecycle, building, testing and shipping high-quality ML-powered solutions
- Build large-scale data pipelines that help researchers train novel ML algorithms, and high-performing systems for online delivery
- Design and optimise multi-agent systems, including tool calling, context management and workflow orchestration
- Partner with research scientists to evaluate, prototype and productionise new concepts
- Lead engagement across Product, UX, Tech and Business, taking ambiguous problems all the way to a system customers can trust
- Mentor engineers and raise the technical bar across the team
What you’ll bring
- 8+ years of software engineering, with hands-on commercial experience in IR, NLP, LLM and agentic systems running in production
- Deep expertise in agentic AI: tool use, multi-agent orchestration, context management, and observability/telemetry (tools like Langfuse or Braintrust will be on their radar)
- Expert production Python: clean, testable, maintainable code, ideally with Pydantic/PydanticAI, plus familiarity with PyTorch and Hugging Face
- Comprehensive evaluation experience: end-to-end quality measurement of agents in live systems, not just offline metrics
- Strong NLP grounding: NER, information extraction, information retrieval
- Experience with MLOps/ModelOps, automation, monitoring and cloud-native apps on AWS, Azure or similar
- Understanding of the maths behind ML, including probabilistic models
- Proven ability to lead stakeholders across Product, UX, Tech and Business
- A track record of mentoring and technical leadership
- Degree in Computer Science or a related field
