What are the responsibilities and job description for the Infrastructure Engineer-NYC, NY / San Francisco, CA-Onsite Work- F2F Interview- Fulltime position at American IT Systems?
- provides AI data infrastructure for financial services, focusing on accurate data consolidation and security.
- The company has strong backing, launched out of stealth, and is supported by experienced founders and top-tier investors.
Role Requirements
- Product Engineer role requires a strong engineering background, ideally from high-growth startups or top tech companies.
- Focus on full-stack engineering with a preference for back-end experience; experience in sophisticated product development is crucial.
Candidate Requirements
- Looking for candidates from top engineering schools, but strong experience can compensate for less prestigious educational backgrounds.
- Experience in high-growth or established tech firms is preferred; a strong engineering culture is essential.
Compensation and Logistics
- Positions are based in SF or NY, with onsite work expected, but flexible hours are allowed.
Timeline and Urgency
- High urgency to hire; open to hiring multiple candidates per role quickly if they meet the criteria.
- Interview process is swift, involving a 30-minute call, a 50-minute coding interview, and a final onsite project-focused interview.
Pain Points
- The main bottleneck is the number of engineers, limiting the ability to take on new business opportunities.
Ideal Candidate Profile
- Motivated by joining an early-stage startup
- Strong technical skills with a history of significant contributions to high-impact projects.
1 - 10 years of experience in cloud or data infrastructure engineering; senior- leaning first NYC hire; 4- 5 years unless top- 25 CS degree or strong- startup pedigree
Experience at a company with a strong, sophisticated engineering bar (e. g. , early team at Stripe, Databricks, Applied Intuition, Decagon, Sierra, Cursor, early Retool, early Scale AI, or equivalent)
One of two archetypes:
Archetype 1: Cloud Infra Engineer deploying across AWS, Google Cloud Platform, Azure.
Archetype 2: Data Infra Engineer building complex data ingestion pipelines.
High slope: demonstrated through strong progression in title/scope changes (e. g. , promoted to senior SWE 1- 2 years early)
ML infrastructure exposure is a strong plus
Regulated industry experience
Degree from a top 25 Computer Science program.
Expertise in at least one major cloud platform (AWS, Google Cloud Platform, Azure) and data pipeline tooling (Databricks, etc. ) with containerization/Kubernetes at scale; data- pipeline tooling is secondary, not the core.
Clear, strong motivation for joining an early- stage startup.
Note: engineers based in NYC should have 4- 5 years of experience unless they have experience working at a strong startup/have a top 25 cs degree
No experience from a company with a strong engineering culture (e. g. , unknown startups, slow- moving companies)
Solely FAANG/big tech experience
About this role
The Role
We're looking for Infrastructure Engineers who will be instrumental in building and securing the backbone of our enterprise-grade AI data platform. You'll design systems that handle large volumes of sensitive financial data under strict security and compliance requirements, including real-time correlation of public data with private, tenant-isolated customer data at scale.
What You'll Do
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Design and build scalable infrastructure to support our AI-powered knowledge engine processing structured and unstructured financial data
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Implement security-first architecture for private cloud deployments, ensuring data governance meets financial services requirements
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Build robust data ingestion pipelines that handle everything from CapIQ feeds [structured data] to internal SharePoint documents [unstructured data]
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Develop monitoring and alerting systems for our BYOC platform
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Implement access controls and audit trails to ensure AI interactions are traceable back to primary sources
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Partner with our AI Research and Product teams to optimize infrastructure for LLM inference and training workloads and building agent infrastructure
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Establish CI/CD practices and infrastructure-as-code for rapid, reliable deployments orchestrated across multiple cloud providers
What You Have
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3-10 years of experience in infrastructure or platform engineering
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Strong background in cloud platforms (AWS, Google Cloud Platform, or Azure) with expertise in containerization and kubernetes
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Security-minded approach with experience in data governance, especially in regulated industries
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Experience with data pipeline technologies and real-time processing frameworks; most any ETL experience will do
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Knowledge of modern monitoring, logging, and observability tools; we use Datadog
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Understanding of compliance requirements (SOC, SOX, GDPR, financial services regulations is a plus)
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Startup mentality with the ability to move fast while building robust, scalable systems
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Experience with AI platforms like SageMaker or Bedrock
Interview Process:
Salary : $150,000