Software Development Engineer Senior

Requirements

• 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; we expect you to 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, including 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: we look for people who find leverage points and improve how work gets done, not only what gets built.
• 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 ambiguity in a large, regulated organization, with the judgment to make trade-offs between scope, speed, and quality.
• Bachelors 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.
• Background in financial services or another regulated enterprise environment.
• Experience with vector databases and agent or orchestration frameworks such as pgvector, Pinecone, Weaviate, Chroma, Lang Chain, Llama Index, 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.
• We value urgency, ownership, humility, strong collaboration, and the ability to work across high-level concepts and fine details.
• We expect high standards for code quality, testing, clarity, and reliability, along with engineering judgment about where each standard matters.
• We value constructive pushback on scope, better alternatives, and protecting quality and team capacity when needed.
• We value staying current as the tooling and model landscape shifts, and helping the people around you do the same.
• We look for visible leaders who are motivated by impact more than titles and team size.

Responsibilities

• Seed and grow the AI Engineering function within GCG technology.
• Partner with business partners and AI engineers to turn ambiguous problems into working AI solutions that improve client-facing capabilities and business outcomes.
• Own end-to-end development for AI applications.
• Lead discovery, design the approach, write code, and own solutions in production.
• Work as a player-coach at the intersection of AI, software, and data.
• Coach and mentor others within the GCG organization as we strive to become an AI-first organization.
• Help set the standard for how generative AI is built and operated responsibly at scale across the firm.
• Hire, structure, and develop a team of AI/ML and agentic software engineers.
• Set the bar for technical quality and team culture.
• Partner directly with business partners to understand workflows, scope opportunities, and translate ambiguous needs into technical specifications.
• Design, build, and operate production generative AI applications such as copilots, assistants, knowledge-search experiences, and agentic workflows.
• Review architecture and work closely with engineers.
• Lead from inside the work, including contributing 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 by defining acceptance criteria, building evaluation harnesses, and measuring correctness, latency, and hallucination.
• Take end-to-end ownership from discovery and design through build, rollout, and operational excellence.
• Instrument systems with observability, cost tracking, and audit trails.
• Apply Fin Ops and cost-optimization practices to AI workloads, tracking and managing token, inference, and infrastructure spend.
• Integrate AI solutions with enterprise data systems, APIs, and MLOps/LLMOps tooling.
• Apply responsible-AI judgment proportionate to the risk of each use case, working with risk and compliance partners to build controls, human-oversight patterns, and audit trails.
• Embed security, privacy, and compliance controls into the systems we build, including identity and access management, encryption, and audit logging.
• Partner with Info Sec 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.
• Produce clear documentation, runbooks, and architectural diagrams so others can understand, operate, and extend the systems we build.
• Technologies
• AI
• Architect
• CI/CD
• Cloud
• IAM
• LLM
• Machine Learning
• MLOps
• Security
• LESS
• More

At Capital Group, we want you to feel comfortable doing great work and bringing your authentic self to everything you do.

We value diverse perspectives and a respectful workplace, and we are committed to fostering a strong sense of belonging.

We offer competitive salary, bonuses, company-funded retirement contributions, generous time away and health benefits from day one, flexible work options, charitable matching gifts, annual grants, and on-demand professional development resources.

This is a full-time Software Engineer - Lead role based in London on a hybrid basis, where we are building our AI Engineering function within GCG technology and working to become an AI-first organization.

We are a global company with more than 9,000 associates in 30+ offices around the world, and we offer a highly competitive compensation and benefits package plus a retirement plan where Capital contributes 15% of eligible earnings.

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