Machine Learning Lead Engineer

Job Title : Machine Learning Lead Engineer

Location : Queens, New York

Experience Required : 12+ Years

Employment Type : Contract

Interview Type : In-Person or Webcam

Job Description

We are seeking a highly experienced Machine Learning Lead Engineer to drive the design, development, and deployment of advanced machine learning models and AI-driven solutions. This role involves leading technical strategy, guiding data science initiatives, and working closely with cross-functional teams to deliver scalable and efficient machine learning systems. The ideal candidate will bring strong leadership, hands-on engineering expertise, and a deep understanding of modern machine learning technologies and tools.

Key Responsibilities

Lead the development, training, testing, and deployment of machine learning and deep learning models.

Architect and implement scalable ML pipelines and production-grade systems.

Collaborate with data engineers, product managers, and software developers to integrate ML capabilities into applications.

Manage model performance, optimization, drift monitoring, and continuous improvements.

Evaluate new algorithms, technologies, and frameworks to enhance system performance.

Oversee the end-to-end ML lifecycle, including feature engineering, data preprocessing, and dataset management.

Guide and mentor junior team members, promoting best practices and code quality.

Document architecture, workflows, and research findings.

Ensure security, compliance, and reliability of data and models.

Work directly with business stakeholders to translate requirements into technical ML solutions.

Required Skills and Qualifications

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or related field.

12+ years of experience in machine learning engineering, data science, or AI solution design.

Expertise in machine learning algorithms, deep learning architectures, and statistical modeling.

Strong programming skills in Python and experience with ML frameworks such as TensorFlow, PyTorch, Scikit-learn, and Keras.

Hands-on experience building production ML pipelines using tools like Kubernetes, Docker, Airflow, or MLflow.

Strong knowledge of cloud platforms such as AWS, Azure, or Google Cloud.

Experience with large-scale data processing using Spark, Hadoop, or similar technologies.

Proficiency in SQL and NoSQL databases.

Ability to present technical concepts to both technical and non-technical stakeholders.

Strong problem-solving skills, analytical thinking, and team leadership abilities.