AI Workflow Engineer

Job Description

AI Workflow Engineer

We are seeking an experienced AI Workflow Engineer to join our team. The successful candidate will be responsible for designing, implementing, and optimizing end-to-end AI/machine learning workflows and pipelines.

Responsibilities

• Architect and build scalable, reliable, and efficient AI/ML workflows and data pipelines
• Integrate AI/ML models into production systems and applications
• Automate and streamline the deployment, monitoring, and maintenance of AI solutions
• Collaborate with data engineers, ML engineers, and DevOps teams for seamless workflow integration
• Optimize AI workflows for performance, scalability, and cost-effectiveness
• Implement best practices for data governance, security, and compliance within AI workflows
• Stay up-to-date with the latest AI workflow tools, technologies, and industry trends

Requirements

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field
• 3+ years of experience in designing and implementing AI/ML workflows and data pipelines
• Strong programming skills in Python, Java, or related languages used for AI development
• Proficiency in AI/ML frameworks and libraries like TensorFlow, PyTorch, scikit-learn
• Experience with cloud platforms (AWS, Azure, GCP) and containerization technologies (Docker, Kubernetes)
• Knowledge of data processing, ETL, and data pipelining tools (Apache Spark, Kafka, etc.)
• Familiarity with DevOps practices and tools (CI/CD, monitoring, infrastructure as code)
• Excellent problem-solving, analytical, and critical thinking abilities
• Strong communication and collaboration skills

Preferred Qualifications:

• Experience with workflow management tools (Apache Airflow, Kubeflow, etc.)
• Knowledge of big data technologies (Hadoop, Spark, etc.)
• Familiarity with machine learning operations (MLOps) principles and practices
• Relevant industry certifications (e.g., AWS Certified Machine Learning Specialty)

The AI Workflow Engineer plays a crucial role in bridging the gap between AI/ML development and production deployment. The ideal candidate should have a strong technical background, hands-on experience in building and optimizing AI workflows, and the ability to collaborate effectively with cross-functional teams to deliver end-to-end AI solutions.