Machine Learning Engineering Consultant - Edinburgh

In this role you will:

• Scale existing ML models into production on a variety of cloud platforms
• Design, develop, test, and deploy data pipelines, machine learning infrastructure and client-facing products and services.
• Provide best-practice knowledge, reference architectures, and patterns for use across ML engineering and architecture communities
• Perform technical architecture assessments, analyse and resolve Analytics/ML related architectural problems
• Working closely with engineering, data science and operations teams to provide improvements and focus areas
• Communicate and provide guidance to senior client leadership and team Contribute ML Engineering expertise to new sales activities

Who we are looking for:

We are looking for technical professionals from a variety of backgrounds with the willingness and ability to learn quickly, think creatively and drive complex ML Engineering problems to a solution.

We offer extensive opportunities for training and upskilling as part our technical career track, however, we’d typically expect experience in at least two of the following areas:

• Hands-on experience in development, deployment and operation of data technologies and platforms such as:
• Cloud Services – AWS, GCP, Azure (and/or others)
• Data platforms – Big Data (e.g. Hadoop, Spark, Hive, Kafka), Data Warehouse (e.g. Teradata, Redshift, BigQuery, Snowflake), batch/streaming/low latency processing
• Platform Engineering – DevOps (Ansible, Jenkins, ELK), Containerisation (Docker, Kubernetes), Integration (APIs, microservices, ETL patterns)  

• Experience in designing and managing key elements of a data and ML platforms: scalable data pipelines, feature stores, data warehouse, metadata, data quality, data security and encryption
• Experience in developing and architecting software across the full lifecycle from prototype to production.
• Experience in data and ML strategy, including analytics portfolio management (including experience in FinOps and Cloud operating model), use case design and definition, migration strategy etc

SRG100