Architect and evolve the Cognitive Runtime, enabling multi-step reasoning, tool usage, and self-correcting agent workflows.
Build and standardise a Skill Catalog so agents can discover and interact with internal APIs and legacy systems seamlessly.
Design scalable memory and context systems, bridging short-term interactions with long-term organisational knowledge.
Develop next-generation AI infrastructure optimised for high-throughput inference, GPU orchestration, and non-deterministic workloads.
Lead Continuous Evaluation frameworks, including automated testing, LLM-as-a-judge systems, and real-time safety monitoring.
Create feedback loops that capture agent interactions to continuously improve model performance and system intelligence.
Collaborate with data teams to build low-latency pipelines that transform raw data into retrieval-ready knowledge for AI systems.
Define semantic governance standards, including embedding strategies, versioning, and caching for reliable agent performance.
Influence engineering teams to adopt agent-ready architectures, ensuring systems are interoperable and machine-readable.
Provide technical leadership on AI ethics, security, and cost efficiency, helping teams deliver responsibly at scale.

Deep expertise in agentic frameworks (e.g. LangGraph, CrewAI, AutoGen) or custom multi-agent orchestration systems.
Strong experience with vector databases, graph databases, and knowledge graph-based retrieval systems.
Proven ability to design and scale AI infrastructure, including inference systems, GPU orchestration, and fine-tuning pipelines.
Solid background in distributed systems, event-driven architectures, and high-scale backend engineering (Python, Go, or Rust).
Strong collaboration skills, working closely with product, data, and engineering leaders to deliver shared outcomes.
Ability to simplify complex systems and create clear, scalable architectural patterns for broader adoption.
Experience building platforms that continuously learn and improve through data feedback loops.
A mindset focused on innovation, challenging the status quo and driving forward-thinking technical solutions.
Strong sense of ownership and accountability, balancing speed with quality and long-term impact.
Passion for shaping the future of AI, with a responsible approach to ethics, safety, and system reliability.

Principal Engineer – AI/ML

Key skills