Lead Java Developer

Category Technologies Backend Java, Spring Boot, REST APIs, Microservices Architecture Distributed Systems, Event-Driven Architecture Cloud AWS Edge/CDN Edge Workers, CDN Functions, Lambda@Edge Languages Java, JavaScript/TypeScript, Python AI/ML ML Inference, Feature Pipelines, Model Serving, Model Registry AI/Data Platform Databricks or equivalent Security/Identity JWT, JWS, PKI, OAuth 2.0, Token Verification Dev Tools GitHub Copilot, LLM-powered workflows, Agentic Coding Assistants Observability Distributed Tracing, Structured Logging, Dashboards . Architect and implement edge-layer solutions using edge-worker runtimes and CDN-layer compute for token detection, header manipulation, and signal capture with minimal latency impact.

Job Role:Staff/ Principal Software Engineer
Location: Hybrid job at St Louis, MO(3 Days Onsite must)
Duration : 6-12+ Months Contract

Role Summary
Position: Staff / Principal Software Engineer
We are looking for a highly experienced Staff/Principal Software Engineer who thrives in ambiguity, enjoys building systems from 0 to 1, and can move quickly from concept to production-ready solutions.
This is a hands-on Individual Contributor (IC) role with significant architectural ownership. You will design systems, write production code, and work at the intersection of edge computing, backend services, distributed systems, and AI/ML platforms. You will collaborate closely with Data Science, Product, and Partner teams to build scalable foundations for trust-signal processing and machine learning inference.
Key Responsibilities

• Design and build end-to-end signal-processing flows, including CDN/edge detection, API ingestion, processing, sandbox storage, and downstream routing.
• Architect and implement edge-layer solutions using edge-worker runtimes and CDN-layer compute for token detection, header manipulation, and signal capture with minimal latency impact.
• Design and develop scalable backend services using Java and Spring Boot, including APIs, signal validation and normalization pipelines, data persistence, and service foundations.
• Define integration patterns for third-party KYA and identity providers, including JWT validation, public-key caching, and deterministic signal ingestion.
• Build platform capabilities supporting ML inference, including structured data-access layers, feature pipelines, model-serving integrations, and model-registry connectivity.
• Partner closely with Data Science teams to move ML solutions from experimentation to production.
• Make architecture and technology decisions related to storage, transport reliability, observability, security, and governance within AWS environments.
• Design scalable microservices and event-driven architectures for reliable signal processing and downstream integrations.
• Take ownership of system design decisions and communicate architecture effectively across engineering and cross-functional teams.
• Work effectively in a fast-paced, startup-like environment with evolving requirements, rapid prototyping, and iterative architecture refinement.

Required Qualifications

• 10+ years of experience building and operating production-grade distributed systems.
• Strong expertise in Java and Spring Boot.
• Extensive experience designing REST APIs, microservices, and event-driven architectures.
• Strong hands-on experience with AWS, including cloud compute, storage, networking, and IAM.
• Hands-on experience with Edge/CDN technologies, such as:
  • Edge Workers
  • CDN Functions
  • Lambda@Edge
  • CloudFront or equivalent edge-compute platforms
• Experience building or supporting ML inference pipelines, including:
  • Data normalization
  • Feature pipelines / feature stores
  • Model serving
  • Model-registry integrations
• Demonstrated experience using AI-assisted development tools, such as:
  • GitHub Copilot
  • LLM-powered development workflows
  • Agentic coding assistants
• Proven ability to build systems from 0 to 1 and work effectively with ambiguous or evolving requirements.
• Strong system-design and architecture skills, with the ability to translate ambiguous product requirements into technical solutions.
• Strong communication and collaboration skills with engineering, product, data science, and partner teams.

Preferred / Nice-to-Have Qualifications

• Experience in identity, trust, or security domains.
• Knowledge of:
  • JWT / JWS
  • PKI
  • OAuth 2.0
  • Token verification
  • Bot detection
• Python experience for data engineering, automation, or scripting.
• Experience with Databricks or similar AI/data platforms.
• Experience implementing observability at scale, including:
  • Distributed tracing
  • Structured logging
  • Monitoring dashboards
• Experience building multi-party integration layers, network services, or platform services.
• Contributions to open-source projects or active participation in technical communities.

Technical Stack

Category	Technologies
Backend	Java, Spring Boot, REST APIs, Microservices
Architecture	Distributed Systems, Event-Driven Architecture
Cloud	AWS
Edge/CDN	Edge Workers, CDN Functions, Lambda@Edge
Languages	Java, JavaScript/TypeScript, Python
AI/ML	ML Inference, Feature Pipelines, Model Serving, Model Registry
AI/Data Platform	Databricks or equivalent
Security/Identity	JWT, JWS, PKI, OAuth 2.0, Token Verification
Dev Tools	GitHub Copilot, LLM-powered workflows, Agentic Coding Assistants
Observability	Distributed Tracing, Structured Logging, Dashboards

The source JD identifies Java Spring Boot, AWS, Edge/CDN, Python, Databricks/ML model registry, and GitHub Copilot/AI-assisted workflows as the core technology stack.
What Success Looks Like
We are looking for engineers who:

• Build AI-enabled platforms, rather than simply consuming AI services.
• Have shipped systems that serve ML models or feed ML inference pipelines.
• Use AI tools and LLMs to significantly improve their engineering productivity.
• Are comfortable operating without perfect specifications and can prototype, ship, learn, and iterate quickly.
• Collaborate effectively with Data Science, Product, Engineering, and Partner teams.
• Influence technical direction through code, architecture, and system design, rather than relying solely on organizational title.