Sr. AI Software Engineer- Investments Technology (Hybrid- Baltimore, MD)
t rowe priceWashington (DC)
Sr. AI Software Engineer- Investments Technology (Hybrid- Baltimore, MD)
Posted today
t rowe priceWashington (DC)
SENIORITY
Lead
About the role
Overview:
In this role you design, build, and deploy autonomous AI systems within Investments Technology to scale responsible AI across the enterprise. You collaborate with cross-functional squads to translate business needs into AI-enabled workflows and automation. You’ll focus on modern cloud-based architectures, agent orchestration, and continuous delivery to drive efficiency and business value. This is a growth-focused opportunity at a premier asset manager that values innovation and collaboration. Compensation / Benefits Competitive compensation Annual bonus eligibility Generous retirement plan Hybrid work schedule Health and wellness benefits, including online therapy Paid time off for vacation, illness, medical appointments, and volunteering days
Responsibilities:
Design, build, test, and deploy AI agents and intelligent systems for cloud-based architectures Partner with business stakeholders to develop agent-driven workflows that automate complex processes and generate insights Champion engineering excellence with AI development best practices, CI, and code quality Contribute to projects on agent orchestration, prompt engineering, real-time data-driven automation, and evaluation frameworks Manage technical debt and drive improvements in AI platforms and infrastructure Proactively seek opportunities to apply AI agents for increased business value and operational efficiency Collaborate with business stakeholders to understand needs and priorities Translate business requirements into effective technology solutions
Key requirements:
BS or MS in Computer Science or related field (or equivalent experience) 5+ years in professional development in an OO language (Java, Python, or JavaScript) Hands-on experience with cloud-native architectures (AWS, Azure) and containerized microservices Strong analytical and problem-solving abilities Commitment to automated testing, code reviews, and continuous delivery Results-driven with a learning mindset collaboration communication with stakeholders curiosity and continuous learning cloud-native architectures (AWS, Azure) containerized microservices agent frameworks
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