Lead Machine Learning Engineer

Overview

We are looking for a highly skilled Lead Machine Learning Engineer to lead our growing ML team. In this role, you will oversee a team of junior ML developers, providing technical guidance, mentorship, and project leadership. You will design and deploy scalable ML systems within our AWS cloud infrastructure , ensuring reliability, performance, and alignment with business objectives.

This is both a hands-on and leadership position —you’ll balance coding, architecture design, and reviews with team management and cross-department collaboration.

Responsibilities

• Lead and mentor a team of junior ML developers, providing technical direction, code reviews, and professional growth guidance.
• Architect, build, and deploy end-to-end ML pipelines on AWS using services such as SageMaker, Lambda, Step Functions, and ECS/EKS.
• Collaborate with product, data, and engineering teams to identify opportunities for ML-driven solutions.
• Oversee model lifecycle management: data preprocessing, feature engineering, training, testing, deployment, and monitoring.
• Establish and enforce best practices for ML development, MLOps, and reproducible research.
• Manage infrastructure as code (Terraform/CloudFormation) for scalable ML environments.
• Implement monitoring, observability, and automated retraining processes for production ML models.
• Optimize cloud costs while ensuring model performance and scalability.
• Stay current with emerging ML tools, frameworks, and AWS offerings; evaluate and introduce them to the team when valuable.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field.
• 5+ years of experience in machine learning engineering, with at least 2 years in a leadership/mentorship role.
• Strong hands-on experience with AWS ML ecosystem (SageMaker, Glue, Athena, Lambda, S3, EC2, Step Functions, etc.).
• Proficiency with Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn, XGBoost).
• Experience with MLOps practices , CI/CD for ML, and ML pipeline orchestration (Kubeflow, MLflow, Airflow).
• Solid understanding of cloud infrastructure and containerization (Docker, EKS/ECS, Kubernetes).
• Strong knowledge of data engineering practices , including ETL, data lakes, and data warehouses.
• Excellent leadership, communication, and organizational skills.

Nice to Have

• AWS Certified Machine Learning – Specialty or AWS Solutions Architect certification.
• Experience with real-time inference systems and large-scale ML deployments.
• Background in NLP, computer vision, or time-series forecasting.
• Prior experience scaling a junior team into a mid-level/high-performing ML group.

Seniority level

Mid-Senior level

Employment type

Full-time

Job function

Engineering and Information Technology

Industries

IT Services and IT Consulting

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