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.

Responsibilities

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.