hackajob
Senior Full Stack Engineer
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Role Overview
As a Senior/Staff AI Full Stack Engineer on the AI products team, you will design, build, and ship production-grade applications powered by Galaxy's AI platform. Your focus is on turning that platform into real products, either by building applications from scratch or by helping teams take their AI prototypes to production.
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.
Key Responsibilities
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
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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
Qualifications
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)
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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
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