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Senior ML Engineer
1 month ago
We are seeking a highly skilled Senior ML Ops Engineer to join our team at Infraveo. This is a remote position that requires a strong foundation in machine learning, cloud computing, and DevOps.
Key Responsibilities- Design and maintain scalable ML infrastructure using cloud platforms like AWS, Azure, or Google Cloud.
- Develop and implement CI/CD pipelines using tools like Jenkins, GitLab CI/CD, or CircleCI to automate the integration and deployment of machine learning models.
- Collaborate with data scientists, data engineers, and software engineers to integrate ML models into production systems, ensuring alignment with business objectives.
- Monitor and maintain the performance of ML models in production using monitoring tools like Prometheus, Grafana, or the ELK stack.
- Manage the versioning and lifecycle of ML models using tools like MLflow, DVC, or TFX, ensuring reproducibility, traceability, and compliance.
- Optimize ML pipelines for performance and cost efficiency, employing techniques such as distributed computing, data parallelism, and automated hyperparameter tuning.
- Ensure the security and compliance of ML models and data pipelines by implementing best practices in data encryption, access controls, and audit logging.
- Conduct experiments and A/B tests to evaluate model performance and iterate on models based on feedback, evolving requirements, and new data.
- Provide technical leadership and mentorship to a team of ML engineers, fostering a collaborative environment and driving continuous improvement in MLOps practices.
- Ability to adapt to new technologies, tools, and methods in the rapidly evolving MLOps landscape.
- Constant drive to innovate and apply the latest ML research to improve processes and solve complex problems.
- Proficiency in Python and SQL for scripting, data manipulation, and programming, using tools like Jupyter Notebooks and Pandas.
- Experience with ML frameworks such as TensorFlow, PyTorch, and Scikit-Learn, for model development and training.
- Hands-on experience with cloud platforms like AWS (SageMaker, Lambda), Azure (ML Studio), or Google Cloud (AI Platform) for deploying and scaling ML models.
- Knowledge of containerization using Docker and orchestration with Kubernetes (K8s) for scalable and automated model deployments.
- Familiarity with CI/CD pipelines using tools like Jenkins, GitLab CI/CD, or CircleCI for automating the deployment and integration of ML models.
- Expertise in data engineering and pipeline creation using technologies like Apache Spark, Apache Airflow, or Kafka for data processing and workflow automation.
- Strong understanding of model monitoring and logging using tools like Prometheus, Grafana, or the ELK stack (Elasticsearch, Logstash, Kibana) to ensure model performance in production.
- Experience with version control using Git/GitHub and model versioning tools like MLflow, DVC, or TFX (TensorFlow Extended).
- Ability to collaborate with cross-functional teams using Agile methodologies and tools like Jira, Slack, or Confluence, aligning ML initiatives with business goals.
- Strong problem-solving and analytical skills for model evaluation, optimization, and addressing deployment challenges, using techniques like A/B testing, hyperparameter tuning, and statistical analysis.
- 5 Days a week work shift.
- Work from home.
