You’ll collaborate with cross-functional teams to deliver high-impact features aligned with enterprise standards and cloud-scale requirements. Own deployment, quality and operation of AI systems, including automated testing, CI/CD pipelines, deployment, and monitoring with strong MLOps and DevOps practices. Troubleshoot live site issues as part of both product development and live site support rotations, ensuring rapid resolution and learning.
Responsibilities
Design and develop highly usable, scalable application capabilities, integrating AI models and enhancing existing features to meet evolving customer needs. Build and debug production-grade code in distributed systems Translate business requirements into AI solutions, collaborating with data scientists, product managers, and engineering teams to ensure alignment and impact Optimize AI model performance and reliability in production environments, including retraining, evaluation, and continuous monitoring. Ensure high reliability and performance of applications and services through intelligent monitoring, alerting, and proactive failover strategies.
Required Qualifications
Bachelor's Degree in Computer Science or related technical field AND 1+ years technical engineering experience with coding in Python for building scalable, production-ready systems. Experience with SQL for querying and managing large datasets; PySpark for distributed data processing and ETL workflows. Hands-on expertise with Power BI for dashboard creation, data modeling, and visualization of contact center metrics. 1+ years of professional experience working with generative artificial intelligence, large language models, or agent-based systems. Master's Degree in Computer Science or related technical field AND 1+ years technical engineering experience with coding in languages C#, Java preferably. Bachelor's Degree in Computer Science or related technical field AI & Domain Expertise: Deep expertise in one or more AI domains, with a proven track record of deploying and scaling AI models in cloud environments. MLOps & LLMOps: Strong experience with MLOps workflows (CI/CD, monitoring, retraining pipelines) and familiarity with modern LLMOps frameworks. Cloud & Infrastructure: Skilled in building and operating infrastructure using Azure, AWS, or Google Cloud, and deploying containerized models with Docker, Kubernetes, or similar tools. Engineering Excellence: Passion for building high-quality, reliable, and maintainable software with strong coding and debugging practices. Collaboration & Communication: Excellent verbal, written, and cross-team communication skills; a collaborative team player across time zones and diverse stakeholder groups. Experience with customers success, zero trust security and compliance Experience with proficient coding, debugging, and problem-solving skills
Original Posting
This role is sourced from Microsoft. Apply on Microsoft careers page