Senior Backend Engineer_Hybrid (NYC) at PulseRise Technologies

New York, New York, United States -

Full Time

Start Date

Immediate

Expiry Date

28 Aug, 26

Salary

0.0

Posted On

30 May, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Go, Distributed Systems, Microservices Architecture, SQL, NoSQL, Graph Databases, RAG Architectures, GenAI, Real-time Data Processing, Kubernetes, Container Orchestration, API Infrastructure, Data Pipelines, System Design, Backend Engineering

Industry

Information Services

Description

Dear applicants, please note that applications without salary expectations and an active LinkedIn profile will not be considered. We are looking for a Senior Backend Engineer to join a fast-moving applied AI and data analytics company as one of their highest-priority hires. The focus is twofold: architecting the platform to sustain significantly increased enterprise usage, and building innovative new features with decisions that hold up for years. This is a DRI (Directly Responsible Individual) role — you will own systems end-to-end, from graph database layer to API infrastructure to data pipelines at scale. The sweet spot is someone senior enough to think architecturally, but still energized by hands-on implementation. Details Schedule: Full-time Location: Hybrid (NYC) Type of collaboration: Full-time employment The platform connects an organization's entire data landscape — internal systems, social media trends, industry reports, consumer behavior signals — into a single coherent intelligence layer that surfaces insights and automates workflows that used to take analysts weeks. At its core is a production graph RAG system connecting temporal and sentiment data at enterprise scale — a key technical differentiator. As a senior backend IC, you will work across the graph database layer, API infrastructure, and data pipelines with concurrency at scale. You will collaborate with ML/AI engineers to bring predictive and prescriptive models into production, own system reliability under high-throughput conditions, and shape the long-term technical trajectory of the platform. There is currently a staff backend engineer working across large parts of the stack — you will be additive senior capacity, not a replacement. You have 5+ years of professional backend engineering experience building systems at scale Strong Python proficiency; confidence that picking up Go would be no problem (Go experience is a plus) Experience with distributed systems and microservices architecture Comfort with both SQL and NoSQL databases and data processing at scale Startup or small-team experience where you built new things with real ownership — not just maintained existing systems Nice to have Graph database experience (a major plus — core to the stack) Go experience Familiarity with RAG architectures and the broader GenAI landscape Experience with real-time data processing, streaming technologies, and concurrency at scale Understanding of ML/AI concepts, particularly forecasting and NLP Kubernetes and container orchestration experience What to do Design, build, and scale backend services in Go and Python powering autonomous intelligence for enterprise clients Architect data ingestion and processing pipelines handling millions of data points across internal and external sources Build and maintain APIs serving the agentic AI platform, supporting real-time decision-making at enterprise scale Work with the graph database layer and production graph RAG system Collaborate with ML/AI engineers to integrate predictive and prescriptive models into production workflows Own system reliability — design for fault tolerance, observability, and performance under high-throughput conditions Contribute to architectural decisions that shape the long-term trajectory of the platform Mentor engineers and help establish backend engineering best practices as the team scales Interview process Recruiter screen Intro call Technical screen with a senior engineer On-site: coding, system design, product sense, AI sense, and a meeting with a co-founder

Responsibilities

Architect and scale backend services, data pipelines, and API infrastructure to support an enterprise AI platform. Collaborate with ML engineers to integrate predictive models and ensure system reliability under high-throughput conditions.