Mid-Level AI/ML Engineer- USA

Cogniify•Los Angeles, CA

111d•$92,400 - $144,900•Remote

About The Position

We are seeking a Mid-Level AI/ML Engineer to design, build, and deploy machine learning models and end-to-end ML pipelines that drive business value. In this role, you will independently own the development of ML solutions from experimentation through production deployment, collaborate with cross-functional teams, and contribute to the maturity of our MLOps practices. The ideal candidate combines strong ML fundamentals with practical engineering skills and a growing ability to make independent technical decisions.

Requirements

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.

Nice To Haves

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.

Responsibilities

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.