- Architect and deliver scalable, resilient AI solutions using agentic systems, LLMs, distributed systems, and event-driven architectures
- Define enterprise-wide technical direction, architecture patterns, integration standards, and reusable platform capabilities
- Lead AI-driven modernization of legacy systems through automated code transformation, service decomposition, and continuous optimization
- Design and implement AI-enabled software development lifecycle practices to improve engineering velocity and quality
- Establish technical standards, governance frameworks, monitoring, evaluation, and responsible AI best practices
- Partner with engineering teams and stakeholders to drive adoption of AI-assisted development and improve system performance and productivity
- Provide technical leadership and mentorship while influencing enterprise technology decisions
- Lead the design, development, and deployment of large-scale, production-grade AI systems and platforms enabling enterprise-wide innovation and modernization
Requirements
- Bachelor's degree in Computer Science, Information Technology, or related field and a minimum of 15 years of experience in software engineering, or an equivalent combination of education and experience
- Deep expertise in distributed systems and large-scale architecture
- Experience delivering production-grade AI/ML systems at scale
- Experience building agentic systems or complex LLM-based applications
- Experience modernizing large, complex legacy systems
- Experience delivering high-quality systems
- Strong programming expertise in Python, Java, or similar
- Deep experience with APIs, microservices, and event-driven architectures
- Experience with cloud platforms, CI/CD, and DevOps practices
- Preferred expertise in healthcare technology ecosystems, AWS, Azure, GCP, AI at Scale, full-stack software engineering, and secure cloud-native architectures
- Hands-on experience with Generative AI, Large Language Models, and modern AI development frameworks and tooling
- Knowledge of cloud infrastructure, IAM, encryption, network security, and cloud security best practices
- Experience driving DevSecOps adoption through automation, CI/CD optimization, infrastructure as code, and secure software delivery
- Ability to influence cross-functional teams and partner with executive and senior leadership
- Candidates must be within a reasonable commuting distance of a posting location unless an accommodation is granted
- Position is not eligible for current or future VISA sponsorship
Core Competencies
Demonstrates expertise in architecting and delivering scalable AI solutions, with a strong focus on distributed systems, large-scale architecture, and AI-driven modernization of legacy systems. Proficient in leading technical direction and implementing best practices for AI-enabled software development and DevSecOps.
Highest-signal resume keywords
- AI/ML Systems Delivery
- Distributed Systems Expertise
- Python Programming
- Cloud Platforms (AWS, Azure, GCP)
- Generative AI and LLMs
ATS Optimization Keywords
Hard Skills
- Software Engineering
- Large-Scale Architecture
- APIs
- Microservices
- Event-Driven Architectures
- CI/CD
- DevOps Practices
- Infrastructure as Code
- Automated Code Transformation
- Service Decomposition
Soft Skills
- Technical Leadership
- Mentorship
- Influencing Cross-Functional Teams
- Collaboration with Executive Leadership
Industry Keywords
- Healthcare Technology Ecosystems
- AI at Scale
- Secure Cloud-Native Architectures
Tools & Technologies
- Cloud Infrastructure
- IAM
- Network Security
- Cloud Security Best Practices
- Modern AI Development Frameworks