Lead Machine Learning Engineer - AI startup

You'll play a key role in shaping the AI strategy and mentoring the machine learning team.\n\nResponsibilities:\n\nLead the end-to-end development of machine learning models, from prototyping to production deployment.\nArchitect scalable ML pipelines and infrastructure.\nWork closely with data scientists to transition research models into robust production systems.\nCollaborate with engineering teams to integrate ML models into applications and services.\nManage and mentor a team of machine learning and data engineers.\nEstablish best practices for model development, evaluation, monitoring, and retraining.\nDesign experiments, analyze results, and iterate rapidly to improve model performance.\nStay current with the latest research and developments in machine learning and AI.\nDefine and enforce ML model governance, versioning, and documentation standards.\n\nRequired Skills & Qualifications:\n\nBachelor's or Master’s degree in Computer Science, Machine Learning, Data Science, Statistics, or a related field (PhD preferred but not required).\n5+ years of professional experience in machine learning engineering.\n2+ years of leadership or technical mentoring experience.\nStrong expertise in Python for machine learning (Pandas, NumPy, scikit-learn, etc.).\nExperience with deep learning frameworks such as TensorFlow , PyTorch , or JAX .\nStrong understanding of machine learning algorithms (supervised, unsupervised, reinforcement learning).\nExperience building and maintaining ML pipelines and data pipelines .\nProficiency in model deployment techniques (e.g., serving models with REST APIs, gRPC, or via cloud services).\nHands-on experience with cloud platforms (AWS, GCP, Azure) for model training and deployment.\nDeep understanding of MLOps concepts: monitoring, logging, CI/CD for ML, reproducibility.\nExperience with Docker and container orchestration (e.g., Kubernetes).\n\nPreferred Skills:\n\nExperience with feature stores (e.g., Feast, Tecton).\nKnowledge of distributed training (e.g., Horovod, distributed PyTorch).\nFamiliarity with big data tools (e.g., Spark, Hadoop, Beam).\nUnderstanding of NLP , computer vision , or time series analysis techniques.\nKnowledge of experiment tracking tools (e.g., MLflow, Weights & Biases).\nExperience with model explainability techniques (e.g., SHAP, LIME).\nFamiliarity with reinforcement learning or generative AI models.\n\nTools & Technologies:\n\nLanguages: Python , SQL (optionally: Scala , Java for large-scale systems)\nML Frameworks: TensorFlow , PyTorch , scikit-learn , XGBoost , LightGBM\nMLOps: MLflow , Weights & Biases , Kubeflow , Seldon Core\nData Processing: Pandas , NumPy , Apache Spark , Beam\nModel Serving: TensorFlow Serving , TorchServe , FastAPI , Flask\nCloud Platforms: AWS (SageMaker, S3, EC2) , Google Cloud AI Platform , Azure ML\nOrchestration: Docker , Kubernetes , Airflow\nDatabases: PostgreSQL , BigQuery , MongoDB , Redis\nExperiment Tracking & Monitoring: MLflow , Neptune.ai , Weights & Biases\nVersion Control: Git (GitHub, GitLab)\nCommunication: Slack , Zoom\nProject Management: Jira , Confluence