THE TEAM YOU’LL BE PART OF

We are seeking an experienced Software Engineer with deep expertise in building and deploying AI and data-driven applications at scale. The ideal candidate brings 10+ years of hands-on experience architecting, developing, and maintaining enterprise-level data and AI systems. You will play a key role in shaping our technical direction, mentoring team members, and delivering robust AI solutions that drive business value.

REQUIRED QUALIFICATIONS

• 10+ years of professional software engineering experience, with proven success in building large-scale data and AI/ML systems.
• Master in Computer Science, Engineering, or related field (Ph. D. preferred).
• Expert-level programming skills in languages such as Python, C++, Java and JavaScript.
• Extensive experience with Azure cloud platform and their services, including infrastructure-as-code and DevOps, DataOps, MLOps and LLMOps practices.
• Strong background in distributed systems, microservices, and container orchestration (Kubernetes).
• Demonstrated expertise in data architecture, including both SQL and NoSQL databases, data processing, and streaming systems.
• Proficiency in infrastructure management, including networking, security, monitoring and Linux administration.
• Strong leadership abilities with experience in mentoring teams and driving technical initiatives.

PREFERRED QUALIFICATIONS

• Ph.D. degree in Computer Science or related field, with focus on Machine Learning/AI.
• Track record of building and maintaining ML platforms, including automated pipelines and monitoring systems.
• Significant contributions to open-source projects or technical communities.

• Architect and implement scalable AI systems and data processing pipelines, including the transformation of research prototypes into production-ready solutions.
• Lead technical decisions for cloud-native infrastructure, data and AI/ML workloads, ensuring system reliability, performance, and cost optimization.
• Design and maintain high-performance APIs, services, and data workflows that power AI-driven features.
• Implement comprehensive monitoring, logging, and alerting systems for both infrastructure, data pipeline and ML models.
• Establish and enforce best practices for infrastructure-as-code, security, compliance, and disaster recovery.
• Drive architectural improvements and modernization initiatives across the technology stack.
• Provide technical leadership through code reviews, mentorship, and collaboration with cross-functional teams.
• Troubleshoot complex production issues and implement robust solutions across the entire stack.