Senior Staff Software Engineer

Spectraforce
North Chicago, IL, United States
6 days ago

Role details

Contract type
Temporary contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
3 years minimum
Working hours
Regular working hours
Job source

Tech stack

Artificial Intelligence Amazon Web Services User Authentication Authentication Protocols C++ (Programming Language) Software Quality Continuous Integration Data Structures Software Debugging Distributed Systems Identity and Access Management Python (Programming Language)
+26 more
Machine Learning Node.Js OAuth OpenID Rapid Prototyping Process Tensorflow Single Sign-On Software Engineering System Programming Management of Software Versions Rust (Programming Language) Graphics Processing Unit (GPU) Cloud Platform System Pytorch Istio Large Language Models Multi-Agent Systems Concurrency Deep Learning Generative AI AI Platforms Kubernetes Information Technology Deployment Automation Golang Programming Languages

Job description

We are seeking a highly skilled Senior Staff Software Engineer to design, build, and scale next-generation AI platforms and intelligent systems. The ideal candidate will possess strong software engineering fundamentals, deep expertise in distributed systems, cloud-native infrastructure, machine learning platforms, and operational excellence. This role involves developing production-grade AI applications, shared services, internal platforms, and foundational infrastructure that support large-scale AI workloads across enterprise environments., * Design, develop, and maintain scalable software systems using C/C++, Python, Go, Rust, Node.js, and other modern programming languages.

  • Build advanced AI solutions including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) pipelines, agentic AI systems, vector databases, and multi-agent architectures.
  • Research, design, and optimize high-performance AI workloads for CPU, GPU, and heterogeneous computing environments.
  • Develop internal platforms, SDKs, shared services, and infrastructure components that accelerate engineering productivity.
  • Create rapid prototypes and production-ready solutions while maintaining high standards of software quality and maintainability.
  • Design and implement inference serving platforms for deep learning and generative AI applications.
  • Establish engineering best practices for testing, debugging, CI/CD, automated deployments, monitoring, observability, incident management, and operational excellence.
  • Drive architecture reviews, technical strategy, and platform modernization initiatives.
  • Collaborate with data scientists, ML engineers, platform engineers, security teams, and product stakeholders to deliver enterprise-scale AI solutions.
  • Manage Kubernetes-based environments including cluster lifecycle management, version upgrades, service mesh administration, GitOps workflows, and Argo-based deployments.
  • Implement secure authentication and authorization mechanisms using SSO, OAuth2, OIDC, SCIM, and enterprise identity governance frameworks.
  • YOE 5+ years

Requirements

  • Years of experience/education and/or certifications required: 5 Years min and AWS Certification
  • What are the top 3-5 skills requirements should this person have?
  • Strong software engineering background with systems programming experience in languages like C/C++, Python, Go, Node, and Rust, plus solid fundamentals in data structures, algorithms, and concurrency.
  • Experience in rapid prototyping with Python, along with knowledge of software engineering principles and foundational computer science.
  • Familiarity with AI/ML development, including fine-tuning large language models, multi-agent systems, RAG pipelines, vector databases, and deep learning frameworks like TensorFlow and PyTorch.
  • Understanding of advanced compute and infrastructure topics such as GPUs, heterogeneous accelerators like cuda, modern inference serving, shared services, internal platforms, testing, CI/CD and Kubernetes.
  • Basic knowledge of identity and access management concepts, including SSO, OAuth/OIDC, SCIM, authentication protocols, access lifecycle management, and identity governance.

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