Interested in this role You can find all the relevant information in the description below.
Chicago, Il
Full-time Onsite role in a mid-sized Financial firm.
The ideal candidate is a pragmatic engineer who enjoys building and deploying solutions that solve real business problems. They possess strong technical judgment, are comfortable working across the full application lifecycle, and understand how to balance speed, innovation, security, and maintainability. This individual thrives in a hands-on environment, is excited by the opportunity to help shape both the solutions being built and the engineering standards that will support future growth and AI adoption
Overview
We are seeking an experienced and hands-on Azure Software & AI Engineer to design, build, deploy, and support modern software applications and AI-enabled solutions within our clients Azure-based technology environment.
This is a highly technical engineering role focused on taking business use cases from concept through production delivery. The successful candidate will help establish engineering standards, deployment patterns, and governance frameworks while building scalable, secure, and maintainable solutions that deliver measurable business value. This is a builder role—not a research position. Candidates with strong software engineering and cloud application development experience are encouraged to apply. Machine learning research, data science expertise, and large-scale people management experience are not required.
Responsibilities
Software & AI Solution Development
Translate business requirements and AI proof-of-concepts into scalable, production-ready applications.
Design, develop, test, deploy, and support cloud-native applications, services, and workflows.
Evaluate, review, and productionize AI-generated code while ensuring quality, security, and maintainability.
Build practical AI-enabled capabilities leveraging Azure OpenAI and related technologies.
Cloud Engineering & DevOps
Develop and deploy solutions within Microsoft Azure environments.
Build and maintain infrastructure using Terraform and Infrastructure-as-Code best practices
Manage environment separation across development, staging, and production environments
Create and maintain CI/CD pipelines using Azure DevOps
Ensure deployments are repeatable, auditable, and aligned with enterprise architecture standards.
Security & Risk Management
Embed security throughout the software development lifecycle, including secure design, coding practices, authentication, authorization, and vulnerability remediation
Identify and mitigate AI-specific risks including prompt injection, insecure tool usage, data leakage, and unsafe outputs
Establish standards and controls for handling sensitive and regulated data
Partner with infrastructure and security teams to ensure compliance with enterprise requirements
Architecture Standards & Enablement
Define engineering standards for application architecture, observability, monitoring, logging, and deployment practices
Develop reusable templates, frameworks, and patterns to accelerate development and improve consistency
Build guardrails that enable safe self-service adoption while maintaining governance requirements
Contribute to the firm's evolving AI engineering, governance, and operating model
Required Qualifications
Strong software engineering background with experience building and supporting production applications
Hands-on experience with Microsoft Azure cloud services
Proficiency with: Python, TypeScript, Terraform (HCL), PowerShell, YAML, SQL
Experience with Azure Kubernetes Service (AKS), DevOps and CI/CD pipeline development
Experience with API development and API Management platforms
Strong understanding of modern software engineering practices including: Infrastructure as Code, Automated testing, Version control, Observability and monitoring, CI/CD
Ability to translate ambiguous business requirements into reliable technical solutions
Excellent communication and collaboration skills.
Preferred Qualifications
Experience building or integrating AI and Large Language Model (LLM) capabilities into production applications
Experience working with Azure OpenAI services
Familiarity with practical AI engineering considerations including data handling, output validation, and AI governance
Experience within financial services, private equity, asset management, or other regulated industries
Experience establishing engineering standards, shared frameworks, templates, or governance models
Experience leading or coordinating third-party vendors and project resources.
Environment
Combination of established production systems and active modernization initiatives. AI-enabled solutions are currently being deployed and supported in production. Primary focus on net-new application development with ongoing operational ownership post-deployment. xsgimln Lean, highly collaborative engineering team environment.