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- Tasks: Develop and deploy AI/ML models, automate workflows, and create microservices.
- Company: Join Ntrinsic Consulting, a leader in IT services and consulting.
- Benefits: Enjoy flexible working options and competitive rates based on experience.
- Why this job: Be at the forefront of AI innovation while collaborating with talented teams.
- Qualifications: Experience with AWS ML services and deploying AI models is essential.
- Other info: Active SC clearance is mandatory; immediate start available.
The predicted salary is between 43200 - 72000 £ per year.
Security Clearance: Active SC or SC Eligible – Mandatory
Start Date: Immediate
Rate: negotiable with experience
You’ll play a critical role in building practical solutions to real-world data science challenges, including automating workflows, packaging models, and deploying them as microservices. The ideal candidate will be adept at developing end-to-end applications to serve AI/ML models, including those from platforms like Hugging Face, and will work with a modern AWS-based toolchain.
Your core responsibilities include:
- Serve as the day-to-day liaison between Data Science and DevOps, ensuring effective deployment and integration of AI/ML solutions.
- Assist DevOps engineers with packaging and deploying ML models, helping them understand AI-specific requirements and performance nuances.
- Design, develop, and deploy standalone and micro-applications to serve AI/ML models, including Hugging Face Transformers and other pre-trained architectures.
- Build, train, and evaluate ML models using services such as AWS SageMaker, Bedrock, Glue, Athena, Redshift, and RDS.
- Develop and expose secure APIs using Apigee, enabling easy access to AI functionality across the organisation.
- Manage the entire ML lifecycle—from training and validation to versioning, deployment, monitoring, and governance.
- Build automation pipelines and CI/CD integrations for ML projects using tools like Jenkins.
- Solve common challenges faced by Data Scientists, such as model reproducibility, deployment portability, and environment standardization.
- Support knowledge sharing and mentorship across data science teams, promoting a best-practice-first culture.
Essential skills:
- Demonstrated experience deploying and maintaining AI/ML models in production.
- Hands-on experience with AWS Machine Learning and Data services: SageMaker, Bedrock, Glue, Kendra, Lambda, ECS Fargate, and Redshift.
- Familiarity with deploying Hugging Face models (e.g., NLP, vision, and generative models) within AWS environments.
- Ability to develop and host microservices and REST APIs using Flask, FastAPI, or equivalent.
- Proficiency with SQL, version control (Git), and working with Jupyter or RStudio.
- Experience integrating with CI/CD pipelines and infrastructure tools like Jenkins, Maven.
- Strong cross-functional collaboration skills and the ability to explain technical concepts to non-technical stakeholders.
Ability to work across cloud-based environments in the following areas:
- Deployment of ML Models or applications using DevOps pipelines.
- Managing the entire ML lifecycle—from training and validation to versioning, deployment, monitoring, and governance.
- Building automation pipelines and CI/CD integrations for ML projects using tools such as Jenkins and Maven.
- Solving common challenges faced by Data Scientists, including model reproducibility, deployment portability, and environment standardization.
Full-Stack Data Scientist AI/ML employer: Ntrinsic Consulting
Contact Detail:
Ntrinsic Consulting Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Full-Stack Data Scientist AI/ML
✨Tip Number 1
Make sure to showcase your hands-on experience with AWS Machine Learning services like SageMaker and Redshift. Highlight specific projects where you've deployed AI/ML models, as this will resonate well with the hiring team.
✨Tip Number 2
Familiarise yourself with Hugging Face models and be ready to discuss how you've integrated them into applications. Being able to explain your approach to deploying these models in AWS environments can set you apart from other candidates.
✨Tip Number 3
Prepare to demonstrate your understanding of the entire ML lifecycle. Be ready to discuss how you've managed model training, validation, and deployment, as well as any challenges you've faced and how you overcame them.
✨Tip Number 4
Emphasise your collaboration skills, especially your ability to communicate technical concepts to non-technical stakeholders. This is crucial for the role, so think of examples where you've successfully bridged that gap.
We think you need these skills to ace Full-Stack Data Scientist AI/ML
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in AI/ML, particularly with AWS services and deploying models. Use specific examples that demonstrate your skills in building and deploying applications.
Craft a Strong Cover Letter: In your cover letter, explain why you're passionate about data science and how your background aligns with the responsibilities outlined in the job description. Mention your experience with tools like Hugging Face and CI/CD pipelines.
Showcase Your Projects: If you have worked on relevant projects, include links or descriptions of these in your application. Highlight any experience with microservices, REST APIs, and automation pipelines to demonstrate your hands-on skills.
Prepare for Technical Questions: Be ready to discuss your technical expertise in detail. Prepare to explain your approach to deploying ML models, managing the ML lifecycle, and solving common challenges faced by Data Scientists.
How to prepare for a job interview at Ntrinsic Consulting
✨Showcase Your Technical Skills
Be prepared to discuss your hands-on experience with AWS Machine Learning services like SageMaker and Redshift. Highlight specific projects where you've deployed AI/ML models, as this will demonstrate your practical knowledge and ability to tackle real-world challenges.
✨Understand the ML Lifecycle
Make sure you can articulate the entire machine learning lifecycle, from training and validation to deployment and monitoring. Being able to explain how you manage these processes will show that you have a comprehensive understanding of what it takes to successfully implement AI/ML solutions.
✨Prepare for Cross-Functional Collaboration
Since the role involves liaising between Data Science and DevOps, be ready to discuss your experience in cross-functional teams. Share examples of how you've communicated technical concepts to non-technical stakeholders, as this is crucial for ensuring effective collaboration.
✨Demonstrate Problem-Solving Skills
Expect questions about common challenges faced by Data Scientists, such as model reproducibility and deployment portability. Prepare to discuss how you've addressed these issues in past projects, showcasing your ability to think critically and find solutions.
