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Specialized MLOps+AI role with mid-level experience requirement and multiple broad tech requirements.
Core MLOps and ML skills transfer across industries despite domain-specific good-to-haves.
Explicit 3+ years plus mandatory MLOps, ML frameworks, Kubernetes, and orchestration tooling requirements.
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Own the end-to-end MLOps lifecycle for computer vision, NLP, and multi-modal AI models including packaging, deployment, monitoring, and rollback.
Design, build, and maintain scalable training and inference pipelines with containerized infrastructure (Docker, Kubernetes) for production AI services.
Develop and operate experiment tracking, model versioning, optimization, and monitoring systems to ensure AI model reliability and efficiency at scale.
3+ years of experience in MLOps, ML infrastructure, or applied AI/ML engineering with exposure to Computer Vision or NLP.
Hands-on experience with workflow orchestration frameworks (preferably Temporal) for fault-tolerant distributed workflows.
Proficiency in Python and ML frameworks like PyTorch, TensorFlow, OpenCV, or HuggingFace Transformers.
Experience with Docker, Kubernetes, CI/CD pipelines for ML (Jenkins, GitHub Actions, etc.), experiment tracking tools (MLflow, Weights & Biases), and cloud environments (GCP, AWS, or Azure).
Experienced in bridging AI research and production deployment, focusing on scalable, reliable ML systems in a startup environment.
Strong background in designing and operating containerized and cloud-based ML infrastructure with modern MLOps best practices.
Comfortable working cross-functionally with research, engineering, and product teams to translate prototypes into deployable AI services.