ML Ops Engineer,
Remote United Kingdom


Remote United Kingdom

Office Address

Project Description

A requirement to join the DXC Analytics and Engineering team as an ML Ops Engineer. You will be joining on the Home Office account as part of our service line, you will be responsible for delivering value and insights from organisational data and for helping companies on their journey to become a data-driven organisation.
- Must be eligible for SC clearance


    Responsibilities include the following:
    • Participate in requirements gathering, technical specification, design and development of complex operationalizing machine learning projects.
    • Contributes to architecture design, development of data or machine learning pipelines, and integration into enterprise systems
    • Responsible for Build and configure multi-tenant machine learning environments on-prem, cloud or hybrid
    • Responsible for Build, test and optimize Machine Learning models
    • Interact with teams of engineers from multiple disciplines Identifying and defining the scope of data science products
    • Defining technical approach, data and algorithms needed
    • Responsible for building out the data product from POC to production-ready system
    • Communicating value, insight, possibilities and limitation of Data Science product for customer and internal stakeholders


Must have

    • 7-10 years experience
    • Individual contributor - Ability to translate business requirements into plausible technical solutions for articulation to other development team members.
    • Basic knowledge of operationalising analytics projects at scale
    • Advanced Experience in python and ML
    • Experience with at least one CI/CD tool, e.g. Jenkins, Github actions, or cloud equivalents
    • Experience in application containerization and orchestration tools - Docker, Kubernetes, or cloud equivalents.
    • Knowledge on a range of Machine Learning and AI techniques (e.g. supervised and un-supervised machine learning techniques, deep learning, graph data analytics, statistical analysis, time series, geospatial, NLP, sentiment analysis, pattern detection, etc.)
    • Experience using Python, R or Spark to extract insights from data
    • Knowledge of SQL for accessing and processing data
    • Experience using the latest Data Science platforms (e.g. Databricks, Azure Machine Learning, AWS SageMaker) and frameworks (e.g. Tensorflow, MXNet, scikit-learn)
    • Software engineering practices (coding practices to DS, unit testing, version control, code review)
    • Hadoop (especially the Cloudera and Hortonworks distributions), and streaming technologies (Kafka, Spark Streaming)
    • Deep understanding of data manipulation/wrangling techniques
    • Delivering insights using visualisation tools (such as Power BI, Qlick) or libraries
    • Experience building and deploying solutions to Cloud (AWS, Azure, Google Cloud)
    • Experience with containerisation and virtualisation (e.g. Docker, Kubernetes, VMs etc.)
    • Kubeflow
    • AWS Sagemaker
    • Google AI Platform
    • Azure Machine Learning
    • Python, Scala

Nice to have

    • Good customer facing skills and ability to clearly communicate technical issues to both technical and non-technical audiences.
    • Can-do, will-do attitude.
    • Hunger to learn new technologies and methodologies.
    • Strong problem solving, analytical and logical skills.
    • Excellent team & communication skills.
    • Ability and desire to share technical experience with colleagues.
    • Demonstrated ability to develop robust enterprise strategies and solutions within timelines.
    • Solid understanding of software design principles and best practices
    • Willingness to learn new skills


English: C2 Proficient



Relocation package

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Work Type

Data Science

Ref Number


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