Lead ML /AI Engineer - Wavicle Data Solutions

WHAT YOU WILL GET TO DO

• Data Engineering and its mechanism, whether it is real time or batch.
• Work with Data Scientists on how models are being built and used against different data inputs.
• Provide a platform for Data Scientist to access data and processes to allow them to fine tune the models.
• Build a system that can provide capabilities for Data Scientists to be contributors.
• Leverage cloud compute to build solutions.
• Understand Data science/ML/AI needs and translate them into workable solutions.
• Mentor and guide other ML engineers, scientists, and senior leaders. Be a continuous learner and invest in the learning and development of ML engineers.
• Lead data strategy and ML/AI strategy projects.
• Develop AI models that align with business goals and support improvements in efficiency and performance for activities in sales, marketing, and business information processes.
• Create data systems for departments or teams to integrate into organizational processes, including data ingestion and transformation infrastructure.
• Plan and develop automated solutions for organizational and technical operations.
• Oversee the development of AI infrastructure for use in consumer systems and products.
• Research and integrate machine and deep learning infrastructure to create security applications that protect sensitive data.
• Collaborate with data scientists, software developers and machine learning engineers to create viable solutions using ML and AI models.
• Lead advanced analytics capabilities for the competency of client.
• Help in writing SOWs, proposals and working with Sales initiatives.
• Interface closely with product management, engineering, DevOps, labeling, and sales teams to build roadmap in supporting the long-term vision of the team.
• Lead a team of highly capable and passionate machine learning engineers, helping them achieve their goals through mentorship.
• Work with large scale computing frameworks, data analysis systems and modeling environments.
• Apply deep learning technologies to give computers the capability to visualize, learn and respond to complex situations.
• Design and implement key components of the Machine Learning Platform infrastructure and establish processes and standard methodologies.
• Work multi-functionally with data scientists, data engineers, and IT teams to design, develop, deploy, and integrate high-performance, production-grade machine learning solutions and data intensive workflows.
• Partner with data scientists and data engineers to create and refine features from underlying data and build reproducible feature pipelines to train models and serve features in production.
• Partner with data platform and operations teams to solve complex data ingestion, pipeline, and governance problems for machine learning solutions.
• Take ownership of production systems with a focus on delivery, continuous integration, and automation of machine learning workloads.
• Provide technical mentorship, guidance, and quality-focused code review to data scientists and ML engineers.

WHAT YOU BRING TO THE TEAM

• Bachelor's Degree in Computer Science, Computer Engineering, Information Systems, or a related field.
• 48+ months of related experience.
• Data, Analytics & AI certifications in Azure, AWS, GCP or SnowFlake.
• 3+ years of experience in the following: Architectural patterns and anti-patterns of large high-scale consumer applications; Developing data and analytics solutions; Developing and deploying machine learning systems into a production environment; Azure Technologies such as Azure Functions, Azure API Management, Azure Even Grid, Azure Service Bus, and AWS Services; Professional engineering in a platform team.
• 4+ years of experience in the following: Professional consulting in IT Programming and application development.
• Telecommuting is permitted. Ability to work 40 hours/week.
• Must also have authority to work permanently in the U.S. Applicants who are interested in this position may apply at www.jobpostingtoday.com Ref# 36252 for consideration.

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