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# Lead AI Engineer / Data Scientist - USA

- **Company:** Qognify, Inc.
- **Location:** Santa Clara, CA, United States
- **Experience:** Expert
- **Salary:** $150,000.0 - $170,000.0
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Artificial Intelligence, Airflow, Amazon Web Services, Computer Vision, Big Data, Continuous Integration, Distributed Computing Environment, Monitoring of Systems, Python (Programming Language), Machine Learning, Performance Tuning, Software Tools, Tensorflow, Prometheus, Azure Machine Learning, Software Deployment, SQL Databases, Data Logging, Feature Store, Feature Engineering, Data Ingestion, Pytorch, Grafana, Apache Spark, Google Vertex AI, Deep Learning, Model Validation, Scikit Learn, Kubernetes, Information Technology, Low Latency, Apache Kafka, Machine Learning Operations, Restful APIs, Software Version Control, Serverless Computing, Recurrent Neural Networks, Docker, Microservices
- **Published:** October 29, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=f789ae2721b14bc7

## About the Role

* Bachelor's or Master's degree in Computer Science, Mathematics, Statistics, Data Science, or a related technical field.
 * 3-5 years of professional experience in machine learning engineering, applied ML, or a closely related role.
 * Strong proficiency in Python and hands-on experience with ML frameworks such as TensorFlow, PyTorch, or scikit-learn.
 * Experience building and deploying ML pipelines in production environments.
 * Working knowledge of MLOps tools and practices including MLflow, Kubeflow, Airflow, or similar orchestration frameworks.
 * Experience with cloud platforms (AWS SageMaker, Azure ML, or GCP Vertex AI) for training, deployment, and serving.
 * Proficiency in SQL and experience working with large-scale datasets.
 * Experience with Docker, Kubernetes, and CI/CD pipelines for ML workflows.
 * Solid understanding of model evaluation, hyperparameter tuning, and feature engineering techniques.
 * Familiarity with REST APIs and microservices architecture for model serving., * Experience with deep learning architectures such as Transformers, CNNs, or RNNs.
 * Familiarity with feature stores (Feast, Tecton) and data versioning tools (DVC, LakeFS).
 * Experience with model monitoring and observability tools (Evidently AI, WhyLabs, or Prometheus/Grafana).
 * Exposure to distributed training frameworks and GPU-accelerated computing.
 * Experience with A/B testing and experimentation frameworks for ML models.
 * Knowledge of data engineering tools such as Spark, Kafka, or dbt.

## Description

* Design, develop, and deploy machine learning models for classification, regression, NLP, computer vision, or recommendation use cases.
 * Build and maintain end-to-end ML pipelines including data ingestion, feature engineering, model training, validation, and serving.
 * Implement MLOps practices including automated training pipelines, experiment tracking, model versioning, and reproducibility.
 * Deploy models to production using containerization (Docker, Kubernetes) and cloud-native services.
 * Monitor model performance in production, implement data drift detection, and manage model retraining workflows.
 * Collaborate with data engineers, software engineers, and product teams to integrate ML solutions into applications and services.
 * Optimize model performance for latency, throughput, and resource efficiency in production environments.
 * Write production-quality code with proper testing, logging, error handling, and documentation.
 * Participate in technical design discussions and contribute to architectural decisions for ML systems.
 * Mentor junior engineers and contribute to team knowledge-sharing and best practices.