Staff Software Engineer

About the role

Job Role: Staff/ Principal Software Engineer

Client: Mastercard

Location: Hybrid job at St Louis, MO(3 Days Onsite must)

Must come for onsite Interview

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.