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- Tasks: Design and deploy scalable machine learning solutions using Python and Amazon SageMaker.
- Company: Join a leading banking client in the financial services sector.
- Benefits: Hybrid working model, competitive salary, and opportunities for professional growth.
- Why this job: Make an impact by integrating innovative ML solutions into real-world applications.
- Qualifications: Proficient in Python and experienced with machine learning libraries and Amazon SageMaker.
- Other info: Collaborative environment with a focus on cutting-edge technology and career advancement.
The predicted salary is between 36000 - 60000 Ā£ per year.
This range is provided by Lorien. Your actual pay will be based on your skills and experience ā talk with your recruiter to learn more.
Base pay rangeDirect message the job poster from Lorien
Employment Type: Full-Time, Fixed Term (18 month FTC)
Location: Home-Based
The Opportunity
This is a high-impact role at the heart of Client\ās digital transformation strategy. As Senior AI Engineer , you will lead the design, development, and deployment of advanced AI solutions that enable smarter decisionāmaking, automation, and innovation across our organisation. You will work on cuttingāedge projects involving generative AI, multiāagent systems, and large language models (LLMs), applying these technologies to realāworld challenges in science and manufacturing.
You will support and help define Client\ās AI technical vision and roadmap, ensuring solutions are scalable, secure, and aligned with business objectives. From building GenAI applications and fineātuning models to designing robust AI infrastructure and governance frameworks, this role offers the chance to shape Client\ās future capabilities. If you thrive on solving complex problems and want to influence how AI transforms industry, this is a great opportunity.
What You\āll Do
Design and develop AIāpowered applications using Python and modern frameworks.
Build and deploy multiāagent AI systems for complex task orchestration.
Transition prototypes to production using tools like LangGraph, AutoGen, CrewAI, and AWS Bedrock.
Fineātune LLMs for domaināspecific use cases using reinforcement learning techniques (PPO, GRPO, DPOā¦).
Develop multiāmodal AI applications integrating text, vision, sounds, and speech models.
Design scalable AI infrastructure and data architectures for large structured and unstructured datasets.
Implement continuous training pipelines to keep models updated for evolving business needs.
Define best practices for AI adoption and embed AI use cases into Client\ās projects.
Collaborate with IT and technical teams to optimise infrastructure for AI deployment.
Monitor emerging AI technologies and recommend innovative solutions for Clients\ās strategic advantage.
Share knowledge and upskill teams to build organisational AI capability.
About YouYou will bring:
Demonstrable expertise in AI and at least two related platforms (e.g., Crew.ai, LangGraph, AutoGen, AWS Bedrock, AgentCore, Azure AI Foundry).
Strong knowledge of prompt engineering, Context Engineering, LLM development, optimisation, and fineātuning techniques.
High proficiency in Python and/or TypeScript, C#, and common ML libraries (TensorFlow, PyTorch, scikitālearn).
Experience designing semantic search and retrieval strategies for generative AI.
Ability to package and deploy models into scalable inference endpoints (REST/gRPC APIs, containerised environments, Kubernetes).
Deep understanding of vector databases, embeddings, and semantic search.
Expertise in Machine Learning and Data Science
Excellent problemāsolving and communication skills, with the ability to translate technical requirements into business solutions.
Qualifications
Degree (or equivalent) in Computer Science, AI, or a related field.
Master\ās degree and experience with cloud platforms (AWS, Azure, Google Cloud) are highly desirable.
Seniority level
MidāSenior level
Employment type
Fullātime
Job function
Information Technology and Consulting
Industries
Data Infrastructure and Analytics
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Machine Learning Engineer employer: Lorien
Contact Detail:
Lorien Recruiting Team
StudySmarter Expert Advice š¤«
We think this is how you could land Machine Learning Engineer
āØTip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. We canāt stress enough how personal connections can lead to job opportunities, so donāt be shy!
āØTip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects. We love seeing practical applications of your work, especially if youāve used Python and Amazon SageMaker.
āØTip Number 3
Prepare for those interviews! Brush up on your ML algorithms and be ready to discuss your experience with CI/CD practices. We want to see your problem-solving skills in action!
āØTip Number 4
Apply through our website! Itās the best way to ensure your application gets seen by the right people. Plus, weāre always on the lookout for passionate candidates like you!
We think you need these skills to ace Machine Learning Engineer
Some tips for your application š«”
Tailor Your CV: Make sure your CV is tailored to the Machine Learning Engineer role. Highlight your experience with Python, Amazon SageMaker, and any relevant projects you've worked on. We want to see how your skills match what we're looking for!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about machine learning and how you can contribute to our team. Keep it concise but engaging ā we love a good story!
Showcase Your Projects: If you've got any personal or professional projects that demonstrate your machine learning skills, make sure to include them. We want to see your hands-on experience and creativity in action!
Apply Through Our Website: Don't forget to apply through our website! Itās the best way for us to receive your application and ensures youāre considered for the role. Plus, it makes the whole process smoother for everyone involved.
How to prepare for a job interview at Lorien
āØKnow Your Tech Inside Out
Make sure you brush up on your Python skills and the ML libraries mentioned in the job description. Be ready to discuss your experience with Amazon SageMaker, as well as any projects you've worked on that involved classification, regression, or forecasting tasks.
āØShowcase Your Problem-Solving Skills
Prepare to share specific examples of how you've tackled complex problems in your previous roles. Think about challenges you've faced while optimising model performance or integrating ML solutions into production systems, and be ready to explain your thought process.
āØFamiliarise Yourself with CI/CD Practices
Since familiarity with CI/CD practices and version control is a requirement, make sure you can discuss how you've implemented these in past projects. Highlight any experience you have with Git and how it has helped streamline your workflow.
āØStay Current with ML Trends
Demonstrate your passion for machine learning by discussing recent research or tools you've come across. This shows that you're not just technically proficient but also genuinely interested in the field and eager to learn more.
