Back
Mid-level AI/MLOps role in metro with broad cloud and model-production requirements drives high competition.
MLOps, cloud, and ML framework skills are broadly transferable across industries, so cross-industry fit is moderate.
Explicit 3–5 years requirement plus mandatory MLOps, cloud, and LangChain/ML framework skills creates strict filters.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Own end-to-end AI platform lifecycle including infrastructure, MLOps pipelines, production deployment, and performance optimization.
Design, build, and maintain AI/ML platform components such as training clusters, feature stores, inference services, and GenAI workflows.
Collaborate with cross-functional teams to deliver scalable, secure AI services integrated with existing systems and document technical designs and procedures.
Bachelor’s or Master’s degree in Computer Science, Electrical/Computer Engineering, or related field.
3+ years of software engineering experience with at least 2 years in building and operating AI/ML platforms.
Strong proficiency in Python (advanced including async and multithreading), plus familiarity with C# or Java.
Experience with cloud platforms (AWS, Azure), containerization (Docker, Kubernetes), infrastructure-as-code (Terraform, ARM templates), and MLOps tools (GitLab CI, Argo CD, MLflow).
Experience delivering production-grade AI services at enterprise scale with strong systems engineering and distributed systems background.
Skilled in integrating AI platforms with data pipelines (Kafka, Azure Data Factory), monitoring solutions (Prometheus, Grafana, ELK), and security/compliance standards (GDPR/CCPA).
Effective in cross-functional collaboration, capable of translating technical concepts for business stakeholders and leading technical presentations and documentation.