Data & AI Solutions Engineer

UTIMCO•Austin, TX

11h

About The Position

We are seeking a Data & AI Solutions Engineer to design, build, and scale enterprise-grade data and AI solutions across UTIMCO's Microsoft ecosystem. This role combines software engineering, cloud technologies, data engineering, and generative AI to deliver intelligent applications that enhance investment decision-making, operational efficiency, and business processes. Success in this role requires curiosity about both technology and the investment business, along with the ability to build trusted partnerships and deliver solutions that improve how the organization operates. Working closely with investment, risk, operations, and technology teams, this individual will translate complex business challenges into secure, scalable, and production-ready solutions while helping advance UTIMCO's enterprise data and AI capabilities. The position emphasizes rapid prototyping with a clear path to enterprise deployment, operational excellence, and long-term support.

Requirements

Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field, or equivalent hands-on software development experience demonstrating strong coding ability.
4+ years of professional software engineering experience, or equivalent practical experience, designing, developing, or supporting software applications, cloud solutions, or data platforms.
Experience developing applications using Azure or AWS, with an interest in working within a Microsoft Azure ecosystem.
Experience working in a modern software development environment using Agile methodologies, including sprint planning, task boards, daily standups, source control, and collaborative code reviews.
Familiarity with Git, GitHub or Azure DevOps, branching strategies, continuous integration, and continuous deployment (CI/CD) practices.
Experience developing full-stack applications using technologies such as React, TypeScript, .NET, or comparable modern development frameworks.
Experience partnering with business users, customers, or stakeholders to understand business needs and translate them into technical solutions.
Experience working with enterprise data, analytics, APIs, or cloud-native applications. Experience with financial or investment data is helpful but not required.
Exposure to AI, machine learning, or generative AI technologies, including using AI-assisted development tools to improve productivity and software delivery.
Familiarity with application security, identity management, testing, debugging, and software engineering best practices.
Strong analytical and problem-solving skills with a passion for learning new technologies and continuously improving.
Excellent written, verbal, and interpersonal communication skills, with the ability to collaborate effectively across technical and non-technical teams.
Demonstrated initiative, curiosity, and ownership. We're looking for someone who is a self-starter, eager to learn, and excited to build solutions in a collaborative environment.

Nice To Haves

Hands-on experience with Azure Data Factory, Databricks, and Microsoft-native analytics solutions
AI- or cloud-related certifications (e.g., Microsoft Azure, Azure AI, or Databricks) are a plus.
Microsoft Azure, Azure AI, Databricks, or other relevant cloud and AI certifications.
Experience operating in financial services, institutional investment management, or other regulated environments.
Experience implementing Retrieval-Augmented Generation (RAG) architectures and enterprise search solutions
Experience developing evaluation, monitoring, and governance frameworks for AI-enabled applications
Familiarity with production support, observability, monitoring, and incident response practices
Knowledge of responsible AI principles, data governance, and ethical use of AI technologies
Experience with Azure DevOps and modern DevOps practices
Working knowledge of institutional investment management, capital markets, or financial services, with an interest in learning investment operations and portfolio management workflows
Experience operating in a regulated enterprise environment
Experience working in a dynamic, collaborative, and team-oriented atmosphere

Responsibilities

Design, develop, and maintain scalable enterprise data pipelines using Azure Data Factory, Databricks, and Azure-native services.
Build reusable data integration and transformation frameworks supporting structured and unstructured data.
Integrate Azure OpenAI Service capabilities into enterprise applications within UTIMCO's secure Azure environment.
Design and implement Retrieval-Augmented Generation (RAG) solutions using approved enterprise data sources.
Develop prompt engineering, evaluation, testing, and monitoring processes that support quality, traceability, and responsible AI deployment.
Design, develop, and deploy full-stack applications using modern web technologies and Microsoft-centric development tools.
Implement and maintain CI/CD pipelines and deployment workflows using Azure DevOps.
Ensure solutions meet enterprise standards for security, performance, observability, reliability, and operational readiness.
Take ownership of solutions throughout their lifecycle by partnering with stakeholders to continuously enhance functionality, usability, and business value.
Partner directly with investment, risk, and operational teams to identify opportunities for workflow automation and business process improvement.
Translate business requirements into scalable technical architectures and iterative implementation roadmaps.
Influence solution design through close partnership with business stakeholders and iterative delivery of high-value capabilities.
Drive adoption through hands-on development, stakeholder collaboration, and continuous feedback loops.
Apply secure-by-design principles and established governance practices throughout the software development lifecycle.
Support responsible and ethical use of data and AI technologies through transparency, monitoring, governance controls, and auditability.
Balance rapid experimentation with disciplined software engineering practices, including automated testing, documentation, monitoring, release management, and operational support.