Dev. Machine Learning Engineering II

Summary

We are seeking a highly skilled and experienced Machine Learning Engineering Developer II to join our team. This role requires a strong understanding of machine learning principles, software engineering best practices, and cloud infrastructure. The ideal candidate will be responsible for the development, deployment, and maintenance of ML models and pipelines in a production environment. 

Key Responsibilities

• Develop, deploy, and maintain machine learning models in production on cloud platforms (specify preferred platforms, e.g., AWS, Azure, GCP). 
• Design, implement, and maintain infrastructure-as-code (IaC) for ML applications, ensuring scalability, reliability, and maintainability. 
• Develop, enhance, and maintain CI/CD pipelines for ML model development and deployment. 
• Build, automate, and maintain machine learning dependencies and artifact pipelines, ensuring efficient and reproducible workflows. 
• Collaborate closely with product teams to accelerate development cycles, improve product robustness, and address production issues. 
• Proactively identify and resolve performance bottlenecks and other issues in ML pipelines and applications. 
• Contribute to the development and improvement of ML engineering best practices and standards. 
• Mentor and guide junior engineers. 
• Proactive Problem-Solving: Identifies critical needs and takes decisive action to address them effectively and efficiently. 
• Innovation and Continuous Improvement: Actively seeks and implements new ideas and innovative solutions, proactively exploring new areas and opportunities for growth. 
• Customer-Centric Approach: Demonstrates a deep understanding of customer needs, anticipating and exceeding expectations to drive customer satisfaction. 
• Analytical and Solution-Oriented: Masterfully applies systematic problem-solving methodologies to complex challenges, consistently delivering effective solutions. 
• Learning Agility and Continuous Development: Proactively investigates mistakes, fosters a culture of learning from experience, and actively seeks opportunities to expand skills and responsibilities. 
• Mentorship and Collaboration: Mentors and guides junior team members, fostering a collaborative environment of knowledge sharing and mutual support.

Critical Qualifications

• Bachelor's degree in Computer Science, Software Engineering, Data Science, or a related field. Relevant experience may be considered in lieu of a degree. 
• 3+ years of experience in a machine learning engineering or related role. 
• Cloud Associate Cloud Engineer certification (e.g., AWS Certified Cloud Practitioner, Azure Fundamentals, Google Cloud Certified Professional Cloud Architect – specify preferred certification). 
• Proficiency in SQL. 
• Understanding of big data processing and parallel processing techniques like Hadoop, Kafka, Apache etc. 
• Experience with containerization technologies (e.g., Docker, Kubernetes). 
• Strong knowledge of cloud computing concepts and services. 
• Experience with CI/CD and DevOps practices. 
• Proficiency in software development lifecycle (SDLC) and machine learning lifecycle (MLLC) methodologies. 
• Strong programming skills in Python or a similar language. 
• Experience with version control systems (e.g., Git). 
• Excellent problem-solving, analytical, and communication skills. 
• Ability to work independently and as part of a team. 

Preferred Qualifications

• Experience with specific cloud platforms (AWS, Azure, GCP – specify preferred platforms). 
• Experience with specific orchestration tools (e.g., Airflow, Argo). 
• Experience with monitoring and logging tools (e.g., Prometheus, Grafana). 
• Contributions to open-source projects.