Space-Eyes
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
#J-18808-Ljbffr
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
#J-18808-Ljbffr