Advisory Engineer, Agentic AI

Description and Requirements

This role is based at Imperial College London. Applicants must be located in London, as the position requires on-site work at least three days per week under our 3:2 hybrid policy.

The Lenovo AI Technology Center (LATC)—Lenovo’s global AI Center of Excellence—is driving our transformation into an AI-first organization. We are assembling a world-class team of researchers, engineers, and innovators to position Lenovo and its customers at the forefront of the generational shift toward AI. Lenovo is one of the world’s leading computing companies, delivering products across the entire technology spectrum, spanning wearables, smartphones (Motorola), laptops (ThinkPad, Yoga), PCs, workstations, servers, and services/solutions.

This unmatched breadth gives us a unique canvas for AI innovation, including the ability to rapidly deploy cutting-edge foundation models and to enable flexible, hybrid-cloud, and agentic computing across our full product portfolio. To this end, we are building the next wave of AI core technologies and platforms that leverage and evolve with the fast-moving AI ecosystem, including novel model and agentic orchestration & collaboration across mobile, edge, and cloud resources.

This space is evolving fast and so are we. If you’re ready to shape AI at a truly global scale, with products that touch every corner of life and work, there’s no better time to join us.

Responsibilities

• Architect agentsystems Designand own the architecture of production agent systems, including the Agent SDK (LangGraph/PydanticGraphs), defining patterns and abstractions that the team builds upon.

• Lead orchestration & routingstrategy Definethe technical vision for orchestration services, model routing (edge-cloud), and multi-agent coordination patterns. Make key architectural decisions on latency/cost/capabilitytrade-offs.

• Drive cross-teamintegration Partnerwith BU product teams (Qira,Tianxi, UDS IQ) to translate requirements into technical specifications. Coordinate with Infrastructure and Data teams on dependencies.

• Establish reliability & safetystandards Defineand enforce guardrail policies, fallback chains, and safety constraints across agent systems. Own incident response and post-mortem processes.

• Build observabilityinfrastructure Designtracing, logging, and monitoring systems that enable the team to understand agent behavior at scale. Create dashboards andalertingfor production systems.

• Mentor and grow theteam Leadtechnical decisions for the squad, mentor junior engineers, conduct code reviews, andestablishengineering best practices and coding standards.

• Shape technicalroadmap Contributeto quarterly planning,identifytechnical risks, and drive initiatives that improve team velocity and system reliability.

Core Skills

• Expert-level Python programming (async patterns, performance optimization, library design) and experience designing APIs and SDKs.

• Deep knowledge of agentic frameworks (LangChain,LangGraph, LlamaIndex,AutoGen) including internals, not just usage.

• Proventrack recordshipping production agent systems serving real users at scale.

• Strong systemdesign skills: distributed systems, state management, message queues, servicemesh patterns.

• Experience with model routing strategies, embedding-based similarity matching, and edge-cloud orchestration.

• Ability to break down ambiguous problems, make architectural decisions independently, and communicate trade-offs clearly.

Bonus Skills

• Experience with MCP (Model Context Protocol) or similar agent communication protocols.

• Background in edge/on-device deployment (mobile, IoT, embedded systems) with latency and memory constraints.

• Contributions to open-source agent frameworks (LangChain, LlamaIndex, etc.).

• Experience building and operating ML platforms orMLOpsinfrastructure.

• Background in Go, Rust, or othersystemslanguages for performance-critical components.

• Published blog posts, talks, or papers on agent systems or LLM engineering.

Qualifications

• 8+ years in software engineering, with at least 2 years focused on ML/AI systems or LLM-based applications (6+ years in software engineering with MS Degree)

• BS/MS in Computer Science or related field; equivalent practical experience considered.

• Track recordof technical leadership: owning systems end-to-end, making architectural decisions, mentoring engineers.

• Experience with production incidents, on-call responsibilities, and post-mortem processes.

• Demonstrated ability to influence technical direction beyond immediate team.

• Opportunities for career advancement and personal development
• Access to a diverse range of training programs
• Performance-based rewards that celebrate your achievements
• Flexibility with a hybrid work model (3:2) that blends home and office life
• Electric car salary sacrifice scheme
• Life insurance

#LATC