Senior Platform Engineer

As an Platform Engineering Specialist, you will be responsible for both building and operating the GenAI Platform and delivering AI use cases into production alongside business teams. You will help create the common platform capabilities that enable AI adoption across the bank while also embedding with business domains to rapidly deliver impactful AI solutions using those capabilities.

This role bridges platform engineering and product delivery, ensuring the GenAI Platform evolves from real-world implementation experiencewhile enabling business teams to safely, quickly, and sustainably deploy AIsolutions at scale.

Key Responsibilities

AI Platform Engineering

• Design, build, and operate core GenAI Platform services that support AI use cases across the bank, including:
  
  1) Orchestration: Agent Runtime, Workflow Orchestration, Tool Calling, Agent Identity.
  
  2) Evaluation: Offline Evaluation, Online Evaluation, Red Teaming, A/B Experimentation.
  
  3) Model Gateway & Registry: Model Gateway, Cost Metering, Model and Agent Registry.
  
  4) RAG & Memory: Embedding Pipelines, Vector Stores, Short-Term and Long-Term Memory.
• Build and maintain the platform's paved-road development experience, providing SDKs, templates, CI/CD pipelines, documentation, and reference implementations that enable teams to build consistently using a common "golden path."
• Embed evaluation directly into the software delivery lifecycle, ensuring AI applications are validated through offline evaluations, LLM-graded assessments, red-team testing, and experimentation before release.
• Implement platform-level governance capabilities, including guardrails, access controls, audit logging, monitoring, observability, and Model Risk Management controls as default platform behaviors.
• Drive platform adoption through a platform-as-a-product mindset, continuously improving developer experience, reliability, security, and operational efficiency.
• Partner closely with Forward Deployed Engineers and AI Operations teams, incorporating production learnings and recurring implementation patterns into the platform roadmap.

Forward Deployed Engineering

• Embed with business domains to identify, shape, and deliver AI use cases from concept through production deployment.
• Work closely with business stakeholders, operations teams, and risk partners to understand business processes, identify opportunities for AI augmentation, and define measurable success criteria.
• Lead use case delivery across the full lifecycle:
  
  1) Discovery & Solution Design: Problem framing, feasibility analysis, human-in-the-loop design, and business case development.
  
  2) Build & Integration: Agent and workflow development, API integration, prompt engineering, retrieval design, and implementation using platform services.
  
  3) Evaluation & Validation: Creation of golden datasets, offline evaluations, LLM-based assessments, and red-team testing.
  
  4) Production & Adoption: Release through platform quality gates and monitor cost, latency, quality, usage, and drift in production.
• Ensure all solutions comply with established governance, security, auditability, and Model Risk Management requirements.
• Champion the use of common platform capabilities and reusable patterns rather than creating one-off solutions.
• Provide feedback from production implementations to improve platform capabilities, reduce delivery friction, and accelerate future use cases.

Required Skills & Experience

• 8+ years of software engineering background with experience designing, building, deploying, and operating production-grade systems.
• Hands-on experience with cloud-native technologies, including the following:
  
  1) AWS
  2) Kubernetes
  3) Terraform
  4) CI/CD and DevOps tooling
• Experience building and deploying AI/GenAI solutions using:
  
  1) Agents and tool calling
  2) Prompt engineering
  3) RAG architectures and vector databases
  4) Workflow orchestration
  5) Evaluation frameworks for non-deterministic systems
• Experience building developer platforms, internal tooling, infrastructure services, or shared technology capabilities.
• Strong understanding of enterprise integrations, APIs, event-driven architectures, and data pipelines.
• Ability to communicate effectively with technical, business, operational, and risk stakeholders.
• Platform-as-a-product mindset with a focus on usability, adoption, reliability, and measurable outcomes.
• Comfortable operating in regulated environments where security, governance, auditability, and risk management are critical requirements.

Role Specific Technical Competencies

• Experience with cloud GPU infrastructure, inference serving, and autoscaled model hosting.
• Experience in consulting, solutions engineering, or forward-deployed delivery environments.
• Familiarity with AI governance, guardrails, model monitoring, and responsible AI practices.
• Exposure to financial services and banking domains such as payments, lending, onboarding/KYC, servicing, collections, or contact center operations.
• Experience building self-service developer platforms and establishing organizational "golden paths."
• Knowledge of observability, tracing, online evaluation, token tracking, and cost management for GenAI workloads.
• Experience designing and executing red-team exercises, human-review workflows, and evaluation programs for AI-powered applications.