Senior Staff AI Engineering
At a glance
Senior AI Engineering role at Egug. London · full-time.
Pay not stated AI Engineering salary
aimlroles summary, based on the employer's posting.
What you'll do
- Set enterprise AI architecture, including patterns for agents, retrieval, model integration, inference, monitoring, evaluation and safety
- Resolve difficult system-design tradeoffs across performance, cost, scale, security, safety and regulatory needs
- Guide AI initiatives across teams and align platform decisions with product, data, infrastructure, risk and security leaders
- Review designs, establish engineering standards and assess emerging technologies for enterprise use
- Coach Staff and Senior Engineers while improving AI platforms and engineering practices
What you bring
- At least twelve years in AI or ML engineering, AI infrastructure, platform or data engineering, with scaled production delivery
- You have designed complete AI platforms spanning data pipelines, model training, deployment, serving, monitoring and inference tuning
- Hands-on Python experience and familiarity with frameworks such as PyTorch, TensorFlow or Scikit-learn
- You have built agentic AI systems and operated GPU-based infrastructure, including workload scheduling and performance tuning
- Experience guiding technical work across teams and developing engineers, including those at Staff level
Who this fits
This role suits a senior technical leader who can set architecture across teams and mentor experienced engineers without direct authority. It is based in London with a hybrid arrangement. You must already be eligible to work in the UK, as visa sponsorship is not available.
From the employer
The Enterprise Technology Services organization partners with every part of the American Express business to power the company’s growth and innovation with trust and efficiency, and drive competitive differentiation with speed. We support the delivery and operations of technology, digital, and data capabilities, platforms, and services globally. Specifically, our team is responsible for the company’s technology engineering, architecture, and infrastructure, providing 24x7 support to ensure an uninterrupted, high-quality experience for customers and colleagues. We also provide product management for core enterprise platforms, and lead technology risk and information security, enterprise data governance and platforms, digital product and design, and enterprise AI platforms on behalf of the company
A Sr Staff AI Engineer leads enterprise-scale AI architecture and implementation across infrastructure and agentic AI systems. The role sets technical direction, ensures scalable, reliable, secure, and responsible AI platforms, mentors senior technical talent, and drives continuous improvement in AI engineering practices and platform capabilities.
Serves as a senior technical authority for enterprise-scale AI systems with significant strategic and long-term business impact
Defines architecture and engineering patterns for agentic AI, retrieval/grounding, model integration, inference platforms, observability, evaluation, and safety
Leads resolution of complex and ambiguous technical challenges, balancing performance, cost, scalability, security, safety, and regulatory constraints
Shapes long-term AI engineering strategy across platforms, tools, and system design approaches
Guides multiple cross-team AI initiatives to ensure coherent architecture, integration, and alignment across domains
Leads technical and architectural reviews, setting standards for engineering excellence and consistent implementation
Partners with Product, Data Engineering, Cloud Infrastructure, Architecture, Risk, and Cybersecurity leaders to align AI solutions with enterprise priorities and governance requirements
Evaluates emerging AI technologies and translates them into scalable, enterprise-ready capabilities
Mentors Staff and Senior Engineers, raising organizational technical capability and developing future technical leaders
MS/PhD in Artificial Intelligence, Machine Learning, Computer Science, or related discipline preferred
Deep knowledge of machine learning and deep learning systems, including model architectures, training, evaluation, and optimization
Advanced knowledge of Generative AI and LLM ecosystems, including embeddings, fine-tuning, prompt design, retrieval-augmented generation, and inference at scale
Strong understanding of AI infrastructure, including GPU platforms, accelerated compute, workload scheduling, capacity optimization, model serving, and inference performance
Advanced understanding of agentic AI design, including planning, reasoning, tool use, memory, multi-agent coordination, and autonomy controls
Strong foundation in distributed systems, cloud-native architecture, Kubernetes, APIs, microservices, event-driven design, observability, and platform reliability
Knowledge of DevOps and CI/CD platforms such as Harness, including automated deployment, environment promotion, governance, and operational controls
Knowledge of enterprise AI governance, including model risk management, explainability, bias detection, safety, and regulatory compliance
Additionnal qualifications
12+ years of experience in AI/ML engineering, AI infrastructure, platform engineering, data engineering, or related fields, with a track record of delivering complex production systems at scale
Proven experience architecting end-to-end AI/ML platforms across data pipelines, training, deployment, serving, monitoring, and inference optimization
Experience designing and operating GPU-based AI infrastructure, including accelerated compute platforms, workload scheduling, utilization optimization, capacity management, and performance tuning
Experience with cloud-native and Kubernetes-based platforms supporting training, batch workloads, inference, orchestration, observability, reliability, and cost efficiency
Hands-on experience with Python and modern AI/ML frameworks such as PyTorch, TensorFlow, Scikit-learn, Hugging Face, and related tooling
Deep experience with agentic AI systems, including planning, tool use, memory, evaluation, retrieval, embeddings, vector databases, and agent frameworks
Experience leading complex cross-functional technical initiatives and influencing architecture and engineering direction across teams without direct authority
Demonstrated ability to mentor and develop engineers at all levels, including Staff-level engineers
Experience working in regulated environments such as financial services, including AI governance, risk management, and compliance considerations
Non-considerations for sponsorship:
Employment eligibility to work with American Express in the UK is required as the company will not pursue visa sponsorship for these positions.