AI Summary
sitevis ltd is hiring a Machine Learning Engineer to design, build, and deploy scalable ML models for data-driven products. This fully remote role involves collaboration with data scientists, software engineers, and product teams. Key responsibilities include developing ML pipelines, optimizing models, and ensuring production reliability.
Key Highlights
Design, develop, and deploy machine learning models for production.
Build and maintain scalable ML pipelines.
Collaborate with cross-functional teams to translate business requirements into ML solutions.
Monitor and retrain models in production.
Write clean, maintainable, and well-documented code.
Technical Skills Required
Benefits & Perks
Fully remote environment
Job Description
About the Role
We are seeking a Machine Learning Engineer to design, build, and deploy scalable ML models that power data-driven products and decisions. You will work closely with data scientists, software engineers, and product teams to take models from research to production in a fully remote environment.
Key Responsibilities
- Design, develop, and deploy machine learning models for production use
- Build and maintain scalable ML pipelines (data ingestion, training, evaluation, deployment)
- Collaborate with product and engineering teams to translate business requirements into ML solutions
- Optimize model performance, reliability, and scalability
- Monitor models in production and retrain as needed
- Conduct experiments and evaluate models using appropriate metrics
- Write clean, maintainable, and well-documented code
Required Qualifications
- Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related field
- 2+ years of experience as a Machine Learning Engineer or similar role
- Strong proficiency in Python
- Experience with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn
- Solid understanding of machine learning algorithms, statistics, and data structures
- Experience with SQL and data manipulation tools
- Familiarity with software engineering best practices (version control, testing, CI/CD)
Preferred Qualifications
- Experience deploying ML models in production
- Knowledge of MLOps tools and workflows
- Experience with cloud platforms (AWS, GCP, or Azure)
- Familiarity with Docker, Kubernetes, and REST APIs
- Experience with big data tools (Spark, Kafka, Airflow)
- Background in NLP, computer vision, or recommendation systems
Tech Stack
- Python, SQL
- PyTorch / TensorFlow
- AWS / GCP / Azure
- Docker, Kubernetes
- Git, CI/CD pipelines