Description

What You'll Do:

• Design, develop, and implement machine learning models and algorithms to solve complex data problems.
• Collaborate with data engineers and data scientists to ensure the effective deployment of ML models.
• Manage the ML Ops lifecycle, including model training, validation, deployment, and monitoring.
• Optimize and maintain existing ML models to improve performance and accuracy.
• Develop and maintain data pipelines to support data ingestion, transformation, and storage.
• Conduct code reviews and provide mentorship to junior team members.
• Stay updated with the latest trends and technologies in AI/ML and data engineering.

Expertise You'll Bring:

• Proven experience in ML Ops, including tools and frameworks such as MLflow, Kubeflow, or similar.
• Strong programming skills in Python, R, or Java.
• Experience with data engineering tools and technologies such as Apache Spark, Hadoop, or Kafka.
• Solid understanding of machine learning algorithms and statistical modeling.
• Familiarity with cloud platforms (AWS, Azure, GCP) and their ML services.
• Experience with version control systems, particularly Git.
• Experience with containerization technologies such as Docker and orchestration tools like Kubernetes.
• Knowledge of data visualization tools (Tableau, Power BI, etc.) and techniques.
• Familiarity with CI/CD pipelines and DevOps practices.
• Experience in working with large datasets and distributed computing.
• Strong analytical and problem-solving skills with a keen attention to detail.
• Excellent communication skills, both verbal and written, with the ability to convey complex technical concepts to non-technical stakeholders.
• Ability to work collaboratively in a team-oriented environment.
• Proactive attitude towards learning and adapting to new technologies.
• Strong organizational skills and the ability to manage multiple priorities effectively.
• Passion for AI/ML and data engineering with a focus on delivering innovative solutions

Key Skills

Education

Any Graduate