IT Software Engineer 4
Description
Job
Title: IT Software
Engineer 4
Chicago (Ideal location), Peoria, Irving,
TX, and Broomfield, CO, but open to 100% remote
Duration: 12 months
Screening
interview during the technical interview, then a 2nd round.
Typical
task breakdown:
- Lead
the architecture, design, development, and testing of modern software
solutions, prototypes, pilots, and reusable platform capabilities.
- Evaluate,
prototype, and apply emerging technologies such as generative AI, agentic
workflows, retrieval-augmented generation, cloud-native services, APIs,
automation, and data-driven application patterns.
- Translate
business opportunities, product goals, and technical requirements into
scalable software designs, solution patterns, technical roadmaps, and
implementation plans.
- Mentor
engineers, provide technical direction, facilitate design reviews, and
promote a culture of experimentation, learning, engineering excellence,
and responsible technology adoption.
- Collaborate
with product managers, architects, data scientists, cybersecurity,
infrastructure, UX, and business stakeholders to deliver solutions aligned
to organizational goals and risk expectations.
- Define
and implement technical evaluation methods, including proof-of-concept
success criteria, model or system performance measures, quality gates,
automated testing, monitoring, and production readiness reviews.
- Identify
technical risks, dependencies, integration needs, security considerations,
and operational constraints early in the solution lifecycle and recommend
practical mitigation strategies.
Work
environment:
2-3 days a
week in the office and for the other offices, it is as needed basis. A
full return could happen at any point.
Education
& Experience Required:
- Bachelor’s
degree (technology-related field) with 8+ years of experience in this
capacity
- No
degree than 10_ years in this capacity.
Required
Technical Skills
(Required)
- Decision
Making and Critical Thinking: Knowledge of the decision-making process and
associated tools and techniques; ability to accurately analyze situations
and reach productive decisions based on informed judgment.
- Software
Development: Knowledge of software development tools and activities;
ability to produce software products or systems in line with product
requirements.
- Software
Development Life Cycle: Knowledge of software development life cycle;
ability to use a structured methodology for delivering and managing new or
enhanced software products to the marketplace.
- Software
Integration Engineering: Knowledge of software integration processes and
functions; ability to design, develop and maintain interfaces and linkage
to alternative platforms and software packages.
- Software
Product Design/Architecture: Knowledge of software product design; ability
to convert market requirements into the software product design.
- Software
Product Technical Knowledge: Knowledge of technical aspects of a software
products; ability to design, configure and integrate technical aspects of
software products.
Software
Product Testing: Knowledge of software product testing; ability to design,
plan, and execute testing strategies and tactics to ensure software product
quality and adherence to stated requirements.
Considerations
For Top Candidates:
- Bachelor’s
degree in Computer Science, Software Engineering, Computer Engineering,
Information Technology, or a related technical discipline; equivalent
practical experience may be considered.
- Progressive
professional experience in software engineering, including hands-on
development of complex applications, distributed systems, APIs, platforms,
or cloud-native services.
- Demonstrated
ability to lead technical work across the full software development
lifecycle, including requirements analysis, architecture, design, coding,
testing, deployment.
- Strong
proficiency in one or more modern programming languages such as Python,
Java, JavaScript/TypeScript, C#, Go, or similar languages.
- Experience
designing scalable, secure, observable, and maintainable software using
modern architecture patterns, APIs, automated testing, CI/CD, and DevOps
practices.
- Working
knowledge of cloud platforms, containerization, infrastructure automation,
integration patterns, and production support practices.
- Ability
to communicate complex technical topics clearly to engineering, product,
architecture, security, and business stakeholders.
- Demonstrated
analytical, problem-solving, and decision-making skills in ambiguous or
rapidly evolving technical environments.
Preferred
Qualifications
- Experience
building or integrating AI, machine learning, generative AI, large
language model, agentic, retrieval-augmented generation, vector database,
or prompt-based solutions.
- Experience
taking emerging technology concepts from proof of concept through pilot,
production hardening, governance review, adoption, and operational
support.
- Familiarity
with responsible AI practices, model evaluation, guardrails, data privacy,
cybersecurity, compliance, and enterprise risk management considerations.
- Experience
with modern cloud services, serverless architectures, Kubernetes,
microservices, event-driven architecture, API management, observability
platforms, and infrastructure-as-code.
- Experience
partnering with product, design, data, security, architecture, and
business teams to identify high-impact use cases and deliver measurable
business outcomes.
- Proven
ability to mentor engineers, influence technical direction without direct
authority, and establish reusable engineering standards, patterns,
frameworks, or accelerators.
- Advanced
degree, cloud certification, AI/ML certification, software architecture
certification, or demonstrated contribution to technical communities,
open-source projects, patents, publications, or internal innovation
initiatives.
Skills
- Agentic AI
- AI
- API
- Automation
- CI/CD
- Cloud
- Cloud Native
- Containerization
- C#
- Cybersecurity
- DevOps
- Distributed Systems
- Event Driven Architecture
- Generative AI
- Infrastructure as Code
- Java
- JavaScript
- Kubernetes
- LLM
- Machine Learning
- Microservices
- Model Evaluation
- Observability
- Proof of Concept
- Python
- RAG
- Serverless
- Test Automation
- TypeScript
- Vector Databases