Machine Learning Operations Engineer

Data is at the heart of everything we do, and as such, we are building a multi-disciplined data team to help us achieve our business goals. This Machine Learning (& Operations) role will drive additional value for our customers by feeding into the delivery of strategic data science solutions whilst setting up resilient and future-proof ML infrastructure and engineering foundations.

Key Responsibilities:

• Designing and developing our ML Operations (MLOps) infrastructure and practices to support the effective transition of machine learning models and PoCs into production.
• Monitoring, and maintaining the health of new and existing ML models post deployment.
• Implementing modern code development and MLOps strategies, in collaboration with Data Science and Data Engineering to support the proactive identification, targeting and resolution of any ML model(s) performance issues across their entire lifecycle.
• Developing operating procedures, work instructions and high-level designs of ML pipelines and architectures to support the effective documentation of our work.
• Developing an MLOps strategy aligned to best practice and existing DevOps practices in the company.
• Developing a strong knowledge of all data sources and maintaining relations with data users/processors across the business.

Qualifications:

• Demonstrable experience of critical thinking, communication and problem-solving ability.
• Experience working in an e-commerce/retail market would be beneficial.
• Strong stakeholder management skills: working collaboratively with Data Science, System, DevOps, Data and Software Engineering teams.
• Excellent communication and interpersonal skills, with the ability to translate complex technical concepts to non-technical stakeholders (Data Engineering to the Business)
• Extensive experience and expertise in Python.
• Proven experience in modern code development practices and implementation of MLOps strategies in the cloud to drive operational and infrastructure cost efficiencies.
• Hands on experience designing, building, and maintaining ML infrastructure and taking ML models to production.
• Working knowledge of CI/CD practices.

Bonus points:

• Experience producing recommender engines and ensemble ML in Google Cloud
• Experience or exposure to managing ML Infrastructure costs and operational reliability in the cloud.
• Willingness to learn new tools and techniques; proactively keeping up to date with latest thinking and practices in the data science space.
• Confident to challenge, and receptive to being challenged.
• Able to establish what does/doesn’t align to business/functional strategies/priorities and communicate dependencies clearly.
• Experience with the Google Cloud Stack (BigQuery, Dataform etc)
• Experience working with CI/CD pipelines through Github
• Experience working in a large data transformation / migration
• Knowledge of advanced analytics techniques, such as predictive modeling and machine learning
• Exposure to cloud-based data storage and processing platforms, such as AWS, Google Cloud, or Microsoft Azure

Who we are: We are a fast-growing business and technology consultant company co-founded in 2019. We offer a custom-tailored, white-glove engineering service fit for our clients, because a digital transformation is more than just technology. With a successful track record of being a preferred vendor for Fortune 500 companies and as a trusted partner for some of the industry’s leading startups, our global talent has helped many clients achieve their goals.

The 3 G’s of RE:

• “Get Shit Done.”
• “Get Over Shit.”
• “Give a Shit.”

Join Our Team and Earn a Generous Sign-On Bonus!

Are you ready to take your career to the next level? We are excited to offer a $2500 Sign-On Bonus to talented professionals who join our dynamic team!