Staff Software Engineer (AI-Focused)

Job Description

Salary: $180,000 to $205,000

About NYSHEX

NYSHEX is transforming the shipping industry through a unified digital infrastructure that connects shippers, carriers, and NVOCCs. Our mission is to solve the problem of contract fulfillment by combining reliability, automation, and transparency. As we scale, were embedding generative AI and intelligent automation across our engineering stackfrom developer productivity and testing to observability and security.

Role Summary

As a Staff Software Engineer at NYSHEX, you will be a senior technical leader who is hands-on with delivery, while influencing system architecture, design decisions, and team mentorship. You will balance deep technical contributions with strategic alignment across engineering, product, and platform initiatives - especially as we evolve into an AI-first engineering organization.

This role is ideal for an experienced engineer who thrives in ownership, collaborative delivery, and is excited by the opportunity to pioneer the use of AI tools in software development, security, documentation, and product innovation. Located in One World Trade Center, NYC this is a hybrid role (In-office Monday through Wednesday) and reports to the CTO.

Key Responsibilities

Technical Leadership & AI-Augmented Execution

Architect and implement complex systems with scalability, performance, and security in mind.

Champion an AI-first approach across design, development, testing, and code reviews.

Leverage GenAI tools like Cursor, GitHub Copilot, Amazon CodeWhisperer or ChatGPT CLI for boilerplate generation, code suggestions, documentation, and testing.

Set and enforce engineering standards, including achieving 85% test coverage.

System Design, Observability & Reliability

Own critical system components and ensure adherence to clean code, modular patterns, and service reliability.

Integrate observability tools (e.g., Datadog or New Relic with anomaly detection) to detect issues proactively.

Lead incident response using incident summaries and LLM-enabled remediation paths.

Delivery & Impact

Deliver high-quality, tested, and well-documented software features on time and in full.

Plan and estimate work collaboratively with product and engineering teams, using a strong understanding of technical tradeoffs.

Identify and resolve tech debt, bottlenecks, and security vulnerabilities before deployment.

Ensure instrumentation and alerting are in place to support continuous improvement.

Mentorship & Technical Enablement

Mentor other engineers through design reviews, pair programming, and AI-assisted collaboration.

Guide responsible integration of GenAI into daily workflows (e.g., code gen, unit test generation, secure coding).

Contribute to shared architectural decision records (ADRs) and technical documentation.

Cross-Functional Collaboration

Work closely with Product Managers, Data Science, and Platform teams to embed AI into customer-facing features and internal tools.

Evaluate and introduce AI agents (e.g., Slack bots, GitHub automation) to accelerate engineering operations.

Tech Stack

Languages & Frameworks:

Java (Spring), Python

JavaScript/TypeScript (Next.js, Node.js) is a plus

REST APIs

Kafka

GraphQL, PostgreSQL, Aurora

Cloud & Infrastructure:

AWS (EC2, S3, Lambda, SES, SNS, SQS, Step Functions, Kafka, etc.)

Kubernetes, Docker

AI Developer Productivity and Quality Tools:

Cursor, GitHub Copilot, CodeWhisperer, ChatGPT CLI

SonarQube (AI rulesets), CodeQL, DeepSource Snyk, AWS Inspector

Monitoring & Observability:

Datadog, New Relic, OpenTelemetry, CloudWatch

CI/CD & Automation:

GitHub Actions, AI-generated tests and release steps Terraform / AWS CDK

Key Performance Indicators (KPIs)

AI-Enabled Software Development Productivity: 50% of PRs use AI-assisted tooling

Code Quality: 85% test coverage and secure code practices

Delivery: Consistent on-time, in-full delivery of key product features

System Reliability: 99.95% uptime for owned services

Mentorship Impact: Regular contribution to team enablement and tooling adoption

Required Qualifications

8+ years of professional experience building scalable, distributed systems

Strong knowledge of backend service architecture, event-driven design, and microservices

Hands-on experience with GenAI tools and integrating AI into engineering workflows

Excellent communication and mentorship skills

Experience with cloud-native development (preferably AWS), CI/CD pipelines, and observability tooling

Preferred Qualifications

Experience with LLM integration (OpenAI, Bedrock, Anthropic)

Exposure to prompt engineering and model lifecycle workflows

Familiarity with security best practices (e.g., OWASP Top 10, OAuth, SAML)

SaaS product experience with a focus on performance, observability, and automation

Contributions to open-source or AI/DevEx tooling communities

What We Offer

Forward-thinking, AI-first engineering culture

Unlimited PTO and flexible hybrid schedule

Health, mental wellness, and family planning benefits

Annual team offsites and internal AI demo days

Opportunities to lead architecture, AI strategy, and engineer enablement

Full-time

Hybrid remote