Drive the development and management of AI and ML infrastructure.
Oversee the end-to-end ML lifecycle — from model training and deployment to monitoring, optimization, and scaling.
Lead a small team of engineers while ensuring seamless collaboration between research, engineering, and operations teams.
Design, maintain, and optimize scalable infrastructure for ML training, inference, and experimentation.
Ensure model deployment pipelines are reliable, efficient, and cost-effective.
Implement robust monitoring, alerting, and automated rollback mechanisms to maintain system reliability.
Partner with research teams to streamline workflows for training, evaluation, and fine-tuning of models.
Support AI-driven initiatives across product teams by providing reliable infrastructure and operational expertise.
Enhance model inference latency, throughput, and cost-efficiency.
Develop and enforce CI/CD practices for ML models, including versioning, testing, and deployment.
Contribute to ML architecture design, documentation, and roadmap planning.

5+ years of hands-on experience in MLOps, ML Engineering, or AI Infrastructure roles.
Strong understanding of ML/DL concepts with applied experience in model training and deployment.
Proficiency with cloud-native ML platforms: AWS SageMaker, GCP Vertex AI, or Azure ML.
Experience with Kubernetes, Docker, MLflow, Kubeflow, or similar orchestration tools.
Familiarity with model optimization techniques: quantization, distillation, TensorRT, FasterTransformer.
Proven ability to lead technical projects and mentor engineers in a fast-paced environment.
Excellent communication and cross-functional collaboration skills.
Ownership-driven mindset and ability to bring clarity to ambiguous technical challenges.

Director of Organic Growth

Key skills