Lead Platform Engineer

Description du poste:

Job Description

Purpose of the role
To design, develop and improve software, utilising various engineering methodologies, that provides business, platform, and technology capabilities for our customers and colleagues.

Accountabilities
* Development and delivery of high-quality software solutions by using industry aligned programming languages, frameworks, and tools. Ensuring that code is scalable, maintainable, and optimized for performance.
* Cross-functional collaboration with product managers, designers, and other engineers to define software requirements, devise solution strategies, and ensure seamless integration and alignment with business objectives.
* Collaboration with peers, participate in code reviews, and promote a culture of code quality and knowledge sharing.
* Stay informed of industry technology trends and innovations and actively contribute to the organization's technology communities to foster a culture of technical excellence and growth.
* Adherence to secure coding practices to mitigate vulnerabilities, protect sensitive data, and ensure secure software solutions.
* Implementation of effective unit testing practices to ensure proper code design, readability, and reliability.

Director Expectations
* Provide expert advice to senior functional management and committees to influence decisions made outside of own function, offering significant input to function wide strategic initiatives.
* Manage, coordinate and enable resourcing, budgeting and policy creation for a significant sub-function.
* Escalates breaches of policies / procedure appropriately.
* Foster and guide compliance, ensure regulations are observed that relevant processes in place to facilitate adherence.
* Focus on the external environment, regulators, or advocacy groups to both monitor and influence on behalf of Barclays, when appropriate.
* Demonstrate extensive knowledge of how the function integrates with the business division / Group to achieve the overall business objectives.
* Maintain broad and comprehensive knowledge of industry theories and practices within own discipline alongside up-to-date relevant sector / functional knowledge, and insight into external market developments / initiatives.
* Use interpretative thinking and advanced analytical skills to solve problems and design solutions in often complex/ sensitive situations.
* Exercise management authority to make significant decisions and certain strategic decisions or recommendations within own area.
* Negotiate with and influence stakeholders at a senior level both internally and externally.
* Act as principal contact point for key clients and counterparts in other functions/ businesses divisions.
* Mandated as a spokesperson for the function and business division.

All Senior Leaders are expected to demonstrate a clear set of leadership behaviours to create an environment for colleagues to thrive and deliver to a consistently excellent standard. The four LEAD behaviours are: L - Listen and be authentic, E - Energise and inspire, A - Align across the enterprise, D - Develop others.

All colleagues will be expected to demonstrate the Barclays Values of Respect, Integrity, Service, Excellence and Stewardship - our moral compass, helping us do what we believe is right. They will also be expected to demonstrate the Barclays Mindset - to Empower, Challenge and Drive - the operating manual for how we behave.

Key Requirements
* Hands-On Technical Leadership: Deliver core platform components (Python services, orchestration, GPU clusters, inference gateways, model registries).
* Technical Strategy & Direction: Define and evolve the technical roadmap for AI enablement-covering generative AI, LLMOps, model serving, and data pipelines.
* Platform at Scale: Apply experience running large-scale distributed platforms with high reliability, cost efficiency, and security.
* Gen AI Expertise: Understand unique challenges in Generative AI (e.g., latency, cost, safety, evaluation, fine-tuning) and design solutions accordingly.
* Plugged into AI Developments: Stay current with emerging AI trends, foundation models, open-source tooling, and best practices; bring insights into platform evolution.
* Governance & Risk Management:
* Embed secure-by-design principles, compliance controls, and auditability into platform architecture.
* Ensure adherence to data privacy, model governance, and AI safety standards.
* Partner with Risk, Compliance, and Cyber teams to mitigate operational and regulatory risks.
* Build High-Quality Systems: Write clean, maintainable code; enforce best practices in CI/CD, testing, observability, and secure coding.
* Mentorship & Talent Development: Coach engineers on advanced AI infrastructure patterns, performance tuning, and cloud-native design.
* Cross-Team Collaboration: Work closely with front-office developers, internal platforms, and infra teams to align technical solutions with business needs.
* Operational Excellence: Drive reliability (SLOs), cost optimization, and incident response for AI platform services.

Key Skills
* Expert-level Python for backend and infrastructure tooling.
* Deep AWS expertise (IAM, KMS, EKS, ECS, Lambda, Glue, Step Functions, MSK, Bedrock).
* Proven experience running platforms at scale (high availability, cost optimization, observability).
* Strong grasp of Generative AI challenges: inference optimization, model lifecycle, evaluation, safety guardrails.
* Ability to lead by doing-hands-on coding, debugging, and performance tuning.
* Awareness of AI/ML infrastructure trends and ability to integrate emerging technologies.
* Strong sense of Governance and Risk Management for AI workloads.

Preferred Tech & Tools
* LLMOps / AI Infra: LiteLLM, Mem0, MLflow, Langfuse for evaluation and tracing.
* Data & Compute: Databricks, SageMaker for model training and deployment.
* Observability & Monitoring: Observe, OpenTelemetry, Prometheus for platform telemetry.
* Vector & Retrieval: FAISS, Weaviate, or equivalent for RAG pipelines.
* GPU Orchestration: Kubernetes (EKS/on-prem), Ray, Slurm for distributed training/inference

Desirable Tech & Tools
* Azure exposure and hybrid cloud strategies.
* On-prem Kubernetes and GPU cluster experience.
* Familiarity with quant workflows and financial data patterns.
* Experience with fine-tuning and custom model deployment