Lead Platform Engineer

Barclays•New York, NY

5d•$220,000 - $300,000

About The Position

To design, develop and improve software, utilising various engineering methodologies, that provides business, platform, and technology capabilities for our customers and colleagues. This role involves building and leading a global front-office engineering success function, writing high-quality Python code for automation and tooling, diagnosing complex issues across cloud and distributed systems, guiding teams on secure AWS usage, enabling AI/ML features, establishing support processes, and mentoring a team of engineers. The position also requires ensuring compliance with governance, model controls, and data privacy requirements.

Requirements

Strong Python engineering experience (backend, tooling, automation, debugging).
Hands-on expertise with AWS: IAM, S3, Lambda, ECS/EKS, Step Functions, CloudWatch.
Proven experience working with Quants, Strats, or front‑office engineering teams.
Solid understanding of AI/ML concepts (inference, vector search, embeddings, RAG, evaluation).
Strong debugging abilities across cloud infra, Python environments, containers, and pipelines.
Excellent communication and stakeholder management skills in high-pressure FO environments.
Experience leading a technical team or owning a global-facing engineering function.

Nice To Haves

Familiarity with tools such as LiteLLM, MLflow, Databricks, SageMaker, Langfuse, Observe, Mem0.
Understanding of financial markets, derivatives, FO analytics workflows, or pricing/risk models.
Exposure to model governance, compliance, and data control frameworks.
Experience with Docker, ECS/EKS, and platform observability tooling.
Experience improving developer productivity or reliability at scale.

Responsibilities

Build and lead the global FO engineering success function across New York, London, and APAC users.
Build trusted relationships with Quants and Strats worldwide, understanding differing desk-specific needs.
Prioritize and manage work intake globally based on business impact and urgency.
Serve as the voice of FO users to influence platform roadmap and feature prioritization.
Write high-quality Python code for automation, integrations, internal utilities, and FO tooling.
Diagnose complex issues across cloud (AWS), distributed systems, Python stacks, notebooks, model pipelines, and APIs.
Deliver engineering solutions that reduce recurring FO friction and accelerate delivery.
Guide global front-office teams on secure and scalable usage of AWS services such as S3, IAM, Lambda, ECS/EKS, Step Functions, CloudWatch, Glue, and CDK/CloudFormation.
Provide hands-on help with debugging, deployment, permissions, networking, and performance.
Help global FO teams onboard onto platform AI features such as inference gateways, embeddings, evaluation tools, and tracing.
Provide guidance on AI/ML concepts: model inference, RAG, embeddings, vector stores, latency optimisation, guardrails.
Support adoption of tools such as LiteLLM, MLflow, Langfuse, Mem0, Databricks, SageMaker, and Observe.
Establish global support processes, workflows, and standards.
Identify patterns in recurring issues and partner with platform engineering to eliminate them via features or automation.
Develop and maintain documentation, onboarding guides, and best practices tailored for FO developers.
Drive proactive monitoring of systems impacting FO teams.
Lead and mentor a small team of engineers focused on FO success.
Set strong engineering standards for code quality, reliability, and FO responsiveness.
Promote a culture of collaboration, empathy, accountability, and excellence.
Ensure all FO support and deployment workflows comply with governance, model controls, and data privacy requirements.
Promote secure-by-default patterns across global FO usage.
Help enforce model governance standards and audit requirements.