Software Engineer - Lead

As a Software Engineer - Lead, you will be responsible for seeding and growing the AI Engineering function within GCG technology.  You will partner with business partners, and AI engineers to turn ambiguous problems into working AI solutions that improve our Client facing capabilities and business outcomes. You be responsible for end to end development including and surrounding our AI applications.  You are the build-and-deploy bridge between the people who own the problem and the AI platform that powers the answer: you lead discovery, design the approach, write the code, and own it in production. You will operate as a player-coach, not a distant manager, working at the intersection of AI, software, and data.  This is a hands-on engineering role for someone who is as comfortable in a working session with a business team as they are building a retrieval pipeline or hardening an agent. As a lead engineer you will also be responsible for coaching and mentoring others withing the GCG organization as we strive to become an AI first organization.  You will help set the standard for how generative AI gets built and operated responsibly at scale across the firm. 

You will 

• Hire, structure, and develop a team of AI/ML and agentic software engineers. Set the bar for technical quality and the culture you want them to work in. 

• Partner directly with business partners to understand their workflows, scope the highest-value opportunities, and translate ambiguous needs into clear technical specifications. 

• Design, build, and operate production generative AI applications such as copilots, assistants, knowledge-search experiences, and agentic workflows, as reliable, production-grade systems rather than demos. 

• Review architecture and work closely with your engineers. Lead from inside the work, not above it, including engineering capabilities and writing code as appropriate. 

• Architect and implement end-to-end retrieval-augmented generation pipelines, including parsing, ingestion, chunking strategy, embeddings, vector storage, retrieval, and prompt management. 

• Build agents and agentic workflows that plan and execute multi-step tasks within explicit, auditable boundaries, with guardrails that keep behavior safe and predictable. 

• Practice eval-driven development: define acceptance criteria up front, build evaluation harnesses, and measure correctness, latency, and hallucination so quality is verifiable and regressions are caught before production. 

• Take end-to-end ownership from discovery and design through build, rollout, and operational excellence. Instrument systems with the observability, cost tracking, and audit trails needed to know when they degrade. 

• Apply FinOps and cost-optimization practices to AI workloads, tracking and managing token, inference, and infrastructure spend so solutions stay cost effective as they scale. 

• Integrate AI solutions with enterprise data systems, APIs, and MLOps/LLMOps tooling, applying sound system design and distributed-systems judgment. 

• Apply responsible-AI judgment proportionate to the risk of each use case, working with risk and compliance partners to build the controls, human-oversight patterns, and audit trails that let the firm move quickly and safely. 

• Embed security, privacy, and compliance controls into the systems you build, including identity and access management (IAM), encryption, and audit logging. Partner with InfoSec and data-governance teams to meet regulatory and internal-policy requirements such as SOC 2 and applicable data-privacy regulations. 

• Codify what works into reusable tools, patterns, and playbooks, and feed insights back to platform, product, and engineering partners so the whole organization gets faster. 

• Produce clear documentation, runbooks, and architectural diagrams so others can understand, operate, and extend the systems you build. 

• Demonstrate the ability to be a full stack engineering and versatility across different development platforms. 

Required qualifications 

• Substantial experience leading software, platform, cloud, or data engineering teams, including building a global team or function from an early stage. 

• Demonstrable hands-on engineering depth; you can still contribute code and lead technical design, not only oversee it. 

• Practical experience applying AI or machine learning to real business problems, ideally including agentic or LLM-based systems. 

• Strong grasp of modern platforms: cataloguing and metadata, semantic and context layers, and data quality across structured and unstructured data. 

• A track record of influencing senior business and technology stakeholders and translating between them. 

• Systems and design thinking: you find leverage points and improve how work gets done, not only what gets built. You solve classes of problems. 

• Excellent written and spoken communication, with the ability to present complex material to diverse audiences. 

• The right to work in the UK and the ability to work from London on a hybrid basis. 

• Open to travel and work across time zones (particularly the US). 

• Hands-on production experience building and shipping LLM-powered applications, including advanced prompt engineering, retrieval, agent development, and evaluation. 

• Strong understanding of system design, APIs, distributed-systems concepts, and cloud-native development, with a track record of owning production systems on solid architectural foundations. 

• High agency and comfort navigating the ambiguity of a large, regulated organization, with the judgment to make trade-offs between scope, speed, and quality. 

• Bachelor’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience. 

• Experience implementing security, privacy, and compliance controls in production systems, for example IAM, encryption, audit logging, and data-governance practices, ideally in a regulated environment. 

Preferred qualifications 

• Background in financial services or another regulated enterprise environment. 

• Experience with vector databases (for example pgvector, Pinecone, Weaviate, or Chroma) and agent or orchestration frameworks (for example LangChain, LlamaIndex, or the Model Context Protocol). 

• Experience with MLOps/LLMOps tooling such as experiment tracking, model versioning, monitoring, evaluation/observability platforms, or CI/CD for ML and LLM systems. 

• Experience implementing responsible-AI or AI-governance controls such as guardrails, human-in-the-loop oversight, and audit trails in production. 

• Experience mentoring engineers or setting technical standards as a senior individual contributor. 

What you bring 

• You operate with urgency, ownership, humility, and strong collaboration. 

• You demonstrate strong communication and influencing skills transitioning between explaining high level concepts and fine details. 

• You hold high standards for code quality, testing, clarity, and reliability, and you apply engineering judgment to know which standard matters where. 

• You can say no constructively by pushing back on scope, proposing better alternatives, and protecting quality and team capacity when it counts. 

• You stay current as the tooling and model landscape shifts, and you help the people around you do the same. 

• You are a visible leader, and the opportunity to make a significant impact means more to you than titles and team size.