Senior Full Stack Engineer

You are a product-minded, full stack engineer who is equally comfortable building a React frontend, a Python backend, and the CI/CD pipeline that ships it. You will act as a trusted advisor to teams across Galaxy, guiding them on best practices at the start of a prototype, and hands-on when it’s time to harden and scale that prototype into a production application. You take end-to-end ownership of development, from application code through to the infrastructure, CI/CD, and deployment that runs it reliably in production, owning the software and infrastructure engineering that gets a product shipped and kept live., Product Engineering, Build

• Need 7+ years of experience.
• Design and build full stack AI-powered applications from scratch, consuming Galaxy’s AI platform (AgentCore, Bedrock, SageMaker) as the underlying intelligence layer
• Build responsive, production-quality frontends in React that deliver a great user experience for AI-driven features
• Build robust, scalable backend services in Python that integrate with LLMs, agentic workflows, and enterprise data sources
• Design and implement data layers using PostgreSQL, Databricks, and Redis as appropriate for the use case

Prototype-to-Production

• Partner with product and engineering teams to take existing AI prototypes and re-architect, harden, and scale them into production-ready applications
• Identify and remediate gaps in security, scalability, reliability, and performance before go-live
• Work within Galaxy’s CI/CD pipelines using Kubernetes and Terraform to ensure applications are deployed, versioned, and scaled reliably
• Implement automated testing and observability (logging, metrics, tracing, alerts) so applications are supportable in production

Product Advisory & Best Practices

• Act as the go-to advisor for teams starting new AI prototypes, providing early guidance on Galaxy’s architectural and product best practices
• Define and evangelize reusable patterns, templates, and starter kits for building on the AI platform
• Review prototype architectures and provide clear, actionable recommendations to reduce rework later in the lifecycle
• Bring a product mindset, balancing user needs, technical feasibility, and time-to-market when advising teams

Collaboration & Enablement

• Work closely with the AI Tech team to stay current on Bedrock platform capabilities and roadmap
• Collaborate with business stakeholders to translate real-world problems into shippable AI product features
• Mentor engineers across teams on full stack best practices for building AI-powered products

Mandatory Technical Skills

Frontend

• React (or similar component frameworks such as Angular, Vue, or Svelte)
• Experience building streaming UIs (SSE/WebSockets) for real-time AI response rendering, avoiding blocking/loading-only UX

Backend

• Python (primary; strong backend experience in a comparable language such as Go, Java, or Node.js considered if you’re ready to work primarily in Python)
• REST APIs
• FastAPI (or similar async Python API frameworks)

CI/CD & Infrastructure

• Kubernetes
• Terraform
• Docker
• Automated testing
• Git and modern branching/PR workflows

Databases & Data Platforms

• PostgreSQL
• Databricks
• Redis

AI & Product

• Strong product sense, able to advise teams on best practices and translate prototypes into scalable product architecture
• Comfortable working across the full stack, from UI to data layer to deployment pipeline

Cloud Platform

• Strong hands-on familiarity with AWS, with recent professional experience built primarily on the AWS stack (not just general cloud exposure)
• Comfortable working alongside AWS-native AI services (Bedrock, SageMaker, AgentCore)
• Experience with core AWS services relevant to full stack product delivery, e.g. EC2, ECS/EKS, Lambda, API Gateway, S3, IAM, CloudWatch, VPC/networking basics

Security

• Experience implementing AuthN/AuthZ (OAuth2/OIDC, SSO), RBAC, and secrets management in production applications

Soft Skills

• Strong communication skills; comfortable presenting architectural recommendations directly to engineering and business stakeholders

Nice to haves

Domain

• Experience working within the financial services domain is preferred
• Prior experience across Capital Markets, Digital Assets/Crypto, or AI infrastructure is highly desirable

AI & Product

• Experience building applications on top of LLM/agentic AI platforms (e.g. AWS Bedrock, SageMaker, or equivalent)
• Familiarity with vector databases and embeddings for AI-powered search/retrieval features
• Ability to evaluate AI feature quality from a product/UX lens (accuracy, latency, hallucination handling, graceful degradation), distinct from model-level evaluation owned by the AI platform team