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- Tasks: Design and develop machine learning processes for various clients.
- Company: Data Reply is a leading data specialist focused on AI and advanced analytics.
- Benefits: Enjoy extensive training, Hackathons, Code Challenges, and a structured graduate programme.
- Why this job: Work with global brands on exciting projects in a diverse and innovative environment.
- Qualifications: 2.1 Bachelor's degree in Engineering or Computer Science; Master's preferred.
- Other info: We are an Equal Opportunities Employer committed to diversity.
The predicted salary is between 28800 - 48000 £ per year.
AI and Machine Learning Engineer
About Data Reply:
DATA REPLY are data specialists, offering data platforms, BI, advanced analytics, and AI/Machine Learning (ML) solutions to drive business success. We specialise in developing, deploying, and operating production data solutions on AWS cloud.
Role overview:
As a Graduate AI & Machine Learning Engineer, you\’ll assist our team in designing and developing machine learning processes across various client environments. Your responsibilities include analyzing client requirements, generating recommendations, and managing the ML lifecycle from data collection and model design to deployment and monitoring. You will collaborate closely with data scientists and senior MLOps Engineers to implement models into production. At Data Reply, you\’ll have access to extensive training and a structured learning path, thriving in a diverse environment with opportunities like Hackathons, Code Challenges, Labcamps, and our graduate program. Working as a Data Reply consultant, you\’ll engage with leading global brands on exciting projects.
Responsibilities:
- Collaborate with Data Science teams to automate and govern machine learning pipelines.
- Manage infrastructure and orchestration pipelines for training and deploying ML models.
- Implement solutions to monitor ML model performance in production.
- Work with cross-disciplinary teams including Data Engineers, Data Scientists, MLOps Engineers, and Data Visualization Specialists.
- Engage with domain experts across industries to understand and address complex problems.
- Analyze and interpret client data, communicating insights effectively to stakeholders.
About the candidate:
- A minimum 2.1 Bachelor\’s degree in Engineering or Computer Science is required; a Master\’s in Data Science or Artificial Intelligence is preferred.
- Excellent communication skills to articulate complex information clearly to diverse audiences.
- Strong understanding of computer science fundamentals (databases, software engineering, cloud computing especially AWS) and data science concepts (machine learning processes).
- Proficiency in Python, including frameworks like PyTorch, TensorFlow, and scikit-learn, with initial knowledge of LangChain and RAGAS.
- Familiarity with CI/CD workflows; experience with Docker and Kubernetes is a plus.
- At least 1 year of relevant experience in training, evaluating, and deploying machine learning models.
- A growth mindset and enthusiasm for tackling new challenges and learning opportunities.
- Flexibility for business travel and positive attitude towards working on diverse client projects.
Reply is an Equal Opportunities Employer committed to diversity and fair recruitment practices. We welcome applicants from all backgrounds and encourage you to inform us of any reasonable adjustments needed during the recruitment process.
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AI and Machine Learning Engineer - employer: Reply
Contact Detail:
Reply Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land AI and Machine Learning Engineer -
✨Tip Number 1
Familiarise yourself with the latest trends in AI and machine learning, especially those relevant to AWS. This will not only help you understand the role better but also give you talking points during interviews.
✨Tip Number 2
Engage with online communities or forums related to AI and machine learning. Networking with professionals in the field can provide insights into the company culture and expectations, which can be beneficial during your application process.
✨Tip Number 3
Consider working on personal projects that showcase your skills in Python and machine learning frameworks like TensorFlow or PyTorch. Having tangible examples of your work can set you apart from other candidates.
✨Tip Number 4
Prepare for technical interviews by practising coding challenges and understanding CI/CD workflows. Being well-versed in these areas will demonstrate your readiness for the role and your ability to contribute effectively from day one.
We think you need these skills to ace AI and Machine Learning Engineer -
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience and skills that align with the job description. Emphasise your proficiency in Python, machine learning frameworks, and any experience with AWS, Docker, or Kubernetes.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for AI and machine learning. Mention specific projects or experiences that demonstrate your ability to collaborate with cross-disciplinary teams and tackle complex problems.
Showcase Your Projects: If you have worked on any relevant projects, include them in your application. Describe your role, the technologies used, and the outcomes achieved. This will help illustrate your hands-on experience in the field.
Prepare for Technical Questions: Anticipate technical questions related to machine learning processes, data analysis, and cloud computing. Brush up on your knowledge of CI/CD workflows and be ready to discuss how you've applied these concepts in previous roles or projects.
How to prepare for a job interview at Reply
✨Showcase Your Technical Skills
Be prepared to discuss your proficiency in Python and any relevant frameworks like PyTorch, TensorFlow, and scikit-learn. You might be asked to solve a coding problem or explain your previous projects, so brush up on your technical knowledge and be ready to demonstrate your skills.
✨Understand the ML Lifecycle
Since the role involves managing the ML lifecycle, make sure you can articulate the steps from data collection to model deployment and monitoring. Familiarise yourself with common challenges in each phase and be ready to discuss how you've tackled similar issues in the past.
✨Communicate Clearly
Excellent communication skills are crucial for this role. Practice explaining complex concepts in simple terms, as you may need to present your ideas to stakeholders who aren't technically inclined. Use examples from your experience to illustrate your points.
✨Demonstrate a Growth Mindset
Express your enthusiasm for learning and tackling new challenges. Share examples of how you've approached learning opportunities in the past, whether through projects, courses, or self-study. This will show that you're adaptable and eager to grow within the company.
