Platform engineer (Member of Technical Staff) at an early-stage, venture-backed AI-native wealth management company, on-site five days a week in Flatiron, NYC. The role owns backend systems, cloud infrastructure (AWS, Docker, Kubernetes, Terraform), data pipelines, and developer tooling that let AI agents run reliably in a regulated financial environment.
Compensation
$200K–$300K / yr
Location
New York, New York, Onsite
Work setup
Onsite
Seniority
Not specified
Employment
Not specified
Experience
5+ years
Category
Devops
Posted
Skills
agentic-ai
ai
api
automation
aws
ci-cd
cloud
containerization
data-ingestion
data-modeling
data-pipelines
developer-experience
distributed-systems
docker
fintech
infrastructure-as-code
kubernetes
llm
postgresql
python
rdbms
rest
terraform
typescript
Must have
5+ years of professional experience in backend, platform, or infrastructure engineering
Strong experience building and operating production backend systems or infrastructure
Experience designing reliable services, APIs, data systems, or distributed infrastructure
Experience deploying and maintaining applications in cloud environments
Ability to own infrastructure and backend systems from design through production operation
Strong understanding of reliability, performance, scalability, and operational trade-offs
Experience improving developer productivity through tooling, automation, CI/CD, or infrastructure abstractions
Ability to diagnose complex technical issues through first-principles problem-solving
Comfort working in a small, fast-moving engineering team
Interest in building foundational systems required to run AI agents in production
Willingness to work on-site in Flatiron, New York City, five days per week
Python: backend services, APIs, automation, or infrastructure tooling
TypeScript: backend services, platform components, or application infrastructure
PostgreSQL: relational database design, query performance, data modeling, and production operations
AWS: designing, deploying, and operating cloud infrastructure
Docker: containerization and reproducible environments
Kubernetes: deploying, scaling, and managing containerized workloads
Terraform: infrastructure as code and repeatable infrastructure configurations
Nice to have
Experience building infrastructure for AI-powered products or agentic systems
Experience designing safe execution environments, sandboxing, or permission boundaries for automated systems
Experience in fintech, wealth management, financial services, or another regulated environment
Experience scaling infrastructure in a small, high-growth startup
Experience integrating multiple data sources or backend systems into a unified platform
Full description
Who is Recruiting from Scratch:
Recruiting from Scratch is a premier talent firm that focuses on placing the best product managers, software, and hardware talent at innovative companies. Our team is 100% remote and we work with teams across the United States to help them hire.
Member of Technical Staff – Platform Engineer
Location
New York, NY
Work Arrangement: On-site, five days per week
Office Location: Flatiron, New York City
Company Stage
Early-stage, venture-backed company
Compensation
- Base Salary: $200,000–$300,000
- Equity: Equity offered
- Bonus: Bonus compensation offered
Visa
Open to eligible visa transfers, including OPT and H-1B transfers. New visa sponsorship is not available.
Company Description
We are building an AI-native wealth management platform designed to transform how financial advisors serve their clients. The platform combines AI-powered agents, client context, and workflow automation to help advisors spend less time on repetitive administrative work and more time delivering personalized advice.
Unlike traditional wealth management systems built around periodic reviews and static financial plans, the platform maintains an evolving understanding of each client's circumstances, remembers important details over time, detects meaningful life changes, and helps advisors respond proactively.
The business was built through the acquisition of established wealth management firms, providing access to real advisors, real clients, and substantial assets under management from the outset. This creates an opportunity to build production systems that operate in a real financial environment rather than relying solely on prototypes or experimental data.
The engineering team is small, technically strong, and focused on shipping high-impact products. Platform infrastructure is central to the business: AI agents rely on the underlying systems to access information, execute workflows, and support client-facing operations reliably and securely.
The company emphasizes ownership, engineering excellence, practical AI implementation, and close collaboration. Engineers are expected to build foundational systems that enable the entire organization to move quickly without compromising reliability, performance, or cost.
What You Will Do
- Own core platform infrastructure: Build and maintain the infrastructure, backend systems, and developer tooling that power the company's AI-driven wealth management platform.
Member of Technical Staff, Platform Engineer at recruitingfromscratch (New York, New York, Onsite) — aijobsearch
CI/CD: building and improving integration and deployment pipelines
Data pipelines: ingestion, transformation, synchronization, or processing
REST APIs: designing and operating backend APIs
Distributed systems: reliability, failure modes, concurrency, and performance
Familiarity with AI agents, LLM-powered applications, or infrastructure supporting AI workloads
Production operations: monitoring, logging, debugging, deployment, incident response
Platform tooling: building tools and abstractions that improve other engineers' experience
- Build reliable backend systems: Design and implement production-grade services, APIs, data pipelines, and supporting infrastructure for a growing AI-native application.
- Create safe execution environments for AI agents: Develop the infrastructure and execution boundaries that allow agents to operate reliably across client workflows and interact with systems of record.
- Design for continuous, large-scale agent activity: Build systems that support agents operating across clients and workflows continuously, with strong reliability, performance, and cost controls.
- Improve platform reliability: Establish and strengthen the monitoring, testing, deployment, and operational practices needed to support systems used in a regulated, high-stakes financial environment.
- Optimize performance and infrastructure costs: Identify bottlenecks, improve system efficiency, and make architectural decisions that balance reliability, latency, scalability, and cost.
- Build developer experience tooling: Create tools, workflows, and infrastructure abstractions that help a small engineering team develop, test, deploy, and operate software efficiently.
- Own cloud infrastructure and deployment systems: Work with AWS, Docker, Kubernetes, Terraform, and CI/CD pipelines to build and operate production infrastructure.
- Develop data infrastructure: Build and maintain data pipelines, database systems, and backend services that support reliable access to client information and platform workflows.
- Enable rapid product development: Remove infrastructure and tooling bottlenecks so engineers can ship AI-powered capabilities without being slowed down by platform limitations.
- Support agent reliability: Build the systems, controls, and operational foundations required for AI agents to interact with sensitive client data and execute workflows predictably.
- Scale the platform through acquisitions: Strengthen the architecture and infrastructure as the business integrates additional wealth management firms and expands across multiple acquired businesses.
- Make foundational architectural decisions: Take ownership of technical choices that affect the reliability, security, performance, cost, and scalability of the platform.
- Work across the engineering stack: Collaborate with application engineers and AI-focused engineers to ensure the platform supports the requirements of production agentic systems.
Ideal Candidate Background
Required Experience
- 5+ years of professional experience in backend engineering, platform engineering, infrastructure engineering, or a closely related field.
- Strong experience building and operating production backend systems or infrastructure.
- Experience designing reliable services, APIs, data systems, or distributed infrastructure.
- Experience deploying and maintaining applications in cloud environments.
- Ability to take ownership of infrastructure and backend systems from design through production operation.
- Strong understanding of reliability, performance, scalability, and operational trade-offs.
- Experience improving developer productivity through tooling, automation, CI/CD, or infrastructure abstractions.
- Ability to diagnose complex technical issues and improve systems through first-principles problem-solving.
- Comfort working in a small, fast-moving engineering team where infrastructure decisions directly affect the entire product.
- Interest in building the foundational systems required to run AI agents in production.
- Willingness to work on-site in Flatiron, New York City, five days per week.
Technical Skills
- Python: Experience building backend services, APIs, automation, or infrastructure-related tooling.
- TypeScript: Experience building backend services, platform components, or application infrastructure using TypeScript.
- PostgreSQL: Experience with relational database design, query performance, data modeling, and production database operations.
- AWS: Experience designing, deploying, and operating cloud infrastructure.
- Docker: Familiarity with containerization and reproducible application environments.
- Kubernetes: Experience deploying, scaling, and managing containerized workloads.
- Terraform: Experience managing infrastructure as code and maintaining repeatable infrastructure configurations.
- CI/CD: Experience building and improving continuous integration and deployment pipelines.
- Data pipelines: Experience building reliable systems for data ingestion, transformation, synchronization, or processing.
- REST APIs: Experience designing and operating backend APIs used by applications, services, or automated systems.
- Distributed systems: Understanding of service reliability, failure modes, concurrency, and system performance.
- AI agents and LLMs: Familiarity with agent frameworks, LLM-powered applications, or infrastructure supporting AI workloads is highly relevant.
- Production operations: Experience with monitoring, logging, debugging, deployment, incident response, and reliability improvements.
- Platform tooling: Ability to build tools and abstractions that improve the development and operational experience for other engineers.
Preferred Background
- Experience building infrastructure for AI-powered products or agentic systems.
- Experience designing safe execution environments, sandboxing, or permission boundaries for automated systems.
- Experience operating systems that execute workflows continuously or at high volume.
- Experience in fintech, wealth management, financial services, or another regulated environment.
- Experience working with sensitive customer data and systems that require strong reliability and access controls.
- Experience scaling infrastructure in a small, high-growth startup.
- Experience building systems that prioritize reliability, latency, and cost as core product requirements.
- Experience integrating multiple data sources or backend systems into a unified platform.
- Experience supporting multiple applications, services, or customer environments on a shared platform.
- Experience with infrastructure consolidation, platform standardization, or integrating systems following acquisitions.
- Experience enabling AI agents to interact with databases, APIs, and systems of record.
- Experience working at a technically demanding startup or engineering-led company.
Working Style and Culture
- High ownership: Takes responsibility for foundational systems and their production outcomes.
- Reliability-first thinking: Understands that infrastructure reliability is central to product quality, especially in financial services.
- Pragmatic engineering: Balances architecture quality with the need to deliver quickly.
- Cost and performance awareness: Treats infrastructure cost, latency, and efficiency as ongoing engineering priorities.
- Developer empathy: Builds tooling and abstractions that make the rest of the engineering team more productive.
- First-principles problem-solving: Investigates underlying causes instead of relying on superficial fixes.
- Comfort with ambiguity: Can identify platform gaps and determine what needs to be built without extensive direction.
- AI-native mindset: Understands the distinctive infrastructure requirements of systems where agents execute workflows and interact with production data.
- Operational discipline: Takes monitoring, incident response, testing, and production readiness seriously.
- Collaborative: Works closely with application and AI engineers to design systems that support real product needs.
- Comfort with a small team: Enjoys broad responsibility and understands that foundational engineering decisions can affect the entire business.
Compensation and Benefits
- Base salary of $200,000–$300,000.
- Equity offered.
- Bonus compensation offered.
- Opportunity to build foundational infrastructure for an AI-native wealth management platform.
- High ownership over backend architecture, infrastructure, developer tooling, and production reliability.
- Opportunity to work with real advisors, clients, and financial workflows from the outset.
- A small, engineering-heavy team with substantial technical responsibility.
- Opportunity to solve infrastructure problems at the intersection of AI agents, data systems, and regulated financial services.
Why Join?
- Build the platform everything depends on: Own the infrastructure and backend systems that enable the product and its AI agents to operate reliably.
- Solve novel infrastructure challenges: Design systems for AI agents that continuously interact with client data and production workflows.
- Make reliability a product feature: Work on performance, reliability, and cost as fundamental requirements rather than secondary concerns.
- Improve developer productivity: Build tooling and infrastructure that allow a small engineering team to ship quickly.
- Work with real-world financial data and workflows: Build against real advisors, clients, and operational needs.
- Have meaningful technical ownership: Make foundational decisions across backend systems, cloud infrastructure, and platform architecture.
- Shape a growing platform: Help strengthen infrastructure as the business scales and integrates additional acquisitions.
- Receive competitive compensation: Earn $200,000–$300,000 in base salary, with equity and bonus opportunities.