Senior Staff AI Engineering

London Source: WhatJobs

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Overview

As Sr Staff AI Engineer, you will shape enterprise-scale AI architecture and implementations across AI systems and platforms. You will set technical direction, ensure scalable and secure AI capabilities, and mentor senior engineers. You’ll collaborate with product, data, cloud, and risk teams to align AI solutions with governance requirements. This role offers impact across critical enterprise AI initiatives, from model deployment to safety and compliance, at scale.

Responsibilities

Serve as a senior technical authority for enterprise-scale AI systems with strategic impact
Define architecture and engineering patterns for agentic AI, retrieval/grounding, model integration, inference platforms, observability, evaluation, and safety
Lead resolution of complex technical challenges balancing performance, cost, scalability, security, safety, and regulatory constraints
Shape long-term AI engineering strategy across platforms, tools, and system design approaches
Guide cross-team AI initiatives to ensure coherent architecture and alignment across domains
Lead technical and architectural reviews, setting standards for engineering excellence
Partner with Product, Data Engineering, Cloud Infrastructure, Architecture, Risk, and Cybersecurity to align AI solutions with governance
Evaluate emerging AI technologies and translate them into scalable enterprise-ready capabilities
Mentor Staff and Senior Engineers to raise organizational technical capability
Qualifications
MS/PhD in Artificial Intelligence, Machine Learning, Computer Science, or related discipline preferred
Deep knowledge of ML and DL systems, architectures, training, evaluation, and optimization
Advanced knowledge of Generative AI and LLM ecosystems, including embeddings, fine-tuning, prompt design, retrieval-augmented generation, and large-scale inference
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
12+ years of experience in AI/ML engineering, AI infrastructure, platform engineering, data engineering, or related fields
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