Job Title: Senior Machine Learning Engineer
Location: Remote
Experience Required: 7+ Years
About the Role
We are seeking an experienced and highly skilled Senior Machine Learning Engineer to design, develop, and deploy advanced ML solutions that solve complex business problems. The ideal candidate will have deep expertise in ML algorithms, data processing pipelines, and scalable production deployments, along with strong problem-solving skills and the ability to mentor junior engineers.
Key Responsibilities
ML Model Development: Design, build, and optimize machine learning models for various business applications such as predictive analytics, NLP, computer vision, recommendation systems, or anomaly detection.
Data Engineering: Develop robust data ingestion, preprocessing, and feature engineering pipelines using large, complex, and multi-modal datasets.
Model Deployment & Scalability: Deploy ML models to production environments ensuring low latency, high availability, and scalability (e.g., using cloud services like AWS Sagemaker, GCP AI Platform, or Azure ML).
Research & Innovation: Stay updated with the latest ML and AI advancements, experiment with cutting-edge algorithms, and recommend their applicability to business needs.
Collaboration: Work closely with data scientists, product managers, software engineers, and stakeholders to translate requirements into scalable ML solutions.
Monitoring & Maintenance: Implement monitoring and retraining pipelines to ensure models remain accurate and relevant over time.
Mentorship: Guide junior team members in best practices, code reviews, and project delivery.
Required Skills & Qualifications
Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or a related field (Ph.D. is a plus).
7+ years of professional experience in ML engineering or applied machine learning.
Proficiency in Python (and relevant libraries such as TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy).
Strong understanding of ML algorithms, deep learning architectures, and statistical modeling.
Experience with data processing frameworks (Spark, Dask, or equivalent) and SQL/NoSQL databases.
Hands-on experience deploying ML models to production (REST APIs, microservices, containerization with Docker/Kubernetes).
Expertise with cloud-based ML platforms (AWS, GCP, or Azure).
Solid understanding of MLOps principles and tools (MLflow, Kubeflow, Airflow, CI/CD pipelines).
Strong problem-solving skills with the ability to handle ambiguous requirements.
Excellent communication and collaboration skills.