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- Tasks: Join a cutting-edge team to build scalable AI systems and innovate in machine learning.
- Company: Aventis Solutions partners with a fast-growing British tech team focused on AI innovation.
- Benefits: Enjoy a competitive salary, bonuses, pension contributions, private healthcare, and flexible hybrid working.
- Why this job: Be part of an elite team, work with advanced tools, and shape the future of AI.
- Qualifications: Strong background in data science, machine learning, or software engineering; proficiency in Python and ML frameworks.
- Other info: Opportunities for long-term career progression and specialisation in advanced AI areas.
The predicted salary is between 52000 - 100000 £ per year.
Aventis Solutions has partnered with a fast-growing British tech team seeking a series of AI/ML Engineers, including Senior, Principal & Team Leads, to build scalable, production-grade AI systems and help shape a forward-thinking machine learning and engineering capability. This is your opportunity to work in an elite innovation team, surrounded by cutting-edge tools and backed by the resources of a global tech leader. We're looking for a range of AI engineers, so if you have a strong background as a data scientist, a data engineer, or a full-stack software engineer, we'd like to hear from you.
Key details:
- Salary: £65,000-£125,000 + 10-40% bonus + 10% pension contribution + private healthcare allowance + strong benefits package
- Location: London HQ + flexible hybrid working model (remote)
- Future Outlook: Long-term career progression toward Lead or Staff Engineer, AI/ML Enterprise Architect, or senior leadership positions. As the AI Innovation Lab grows, there will be ample opportunity to specialise in advanced areas such as LLM systems, MLOps, or cloud AI platforms, or to take on broader product or technical leadership roles.
Key Skills, Attributes & Tech Desired:
- Builder: Solid background in building and deploying data science, machine learning or statistical models at scale, ideally within product environments or a data-rich business.
- Tech-savvy: Proficient in Python and modern ML frameworks (e.g., PyTorch, TensorFlow, JAX). Strong command of cloud-native AI tooling (e.g., AWS SageMaker, GCP Vertex AI, AzureML/AzureAI, MLflow, Weights & Biases).
- Well-rounded Engineer: Comfortable working with version-controlled codebases, DevOps pipelines, containerisation/microservices (Docker/Kubernetes), and Infrastructure-as-Code (Terraform). Cursor AI is widely used too.
- Data-driven: Experience working with big data systems like Databricks, Spark, Kafka, Snowflake, or BigQuery. Familiarity with batch and streaming ML pipelines is ideal.
- LLM-curious: Hands-on exposure to LLMs or interest in working on generative AI use cases (e.g., embeddings, retrieval-augmented generation, fine-tuning). Exposure to the likes of LangChain, OpenAI API, Anthropic Claude, Mistral, Groq, Cohere, vLLM or similar is desirable.
- Innovator: Passionate about experimentation and new techniques. You enjoy solving complex problems, building reusable ML components, and staying up to date with frontier AI tools and architecture: agentic AI.
- Collaborative: A great team player who works well with cross-functional colleagues in product, data engineering, and DevOps. You're comfortable pair-programming, reviewing code, and participating in agile ceremonies.
- Commercial: You understand the impact of artificial intelligence and machine learning on real-world outcomes and enjoy shaping systems that make a tangible difference to users or business metrics.
- Communication: Able to clearly explain complex concepts to a range of audiences. Comfortable presenting ideas, sharing research findings, or demoing model performance.
- Regulated Industry Exposure: Background or exposure to domains like banking, insurance, FinTech, energy or asset management is a plus, but it is not required.
Role Overview:
AI/ML Engineers: AI Innovation Team: You'll help build the technical backbone of this next-generation AI lab, contributing to everything from data pipelines and training infrastructure to the deployment of advanced AI systems and LLM-powered applications. We're looking for any of the following: Data Scientist, Python Engineer, MLOps Engineer, AI Engineer, Machine Learning Engineer, Data Engineer, Full-stack software engineers and similar to become forward-thinking innovators. The team is highly technical, research-oriented, and focused on experimentation. The company is already investing heavily in AI infrastructure, model tooling, and R&D environments. So, you’ll play a critical role in designing, training, evaluating, and deploying production-ready models while contributing to reusable architecture and automation for future scalability.
Interested? Please submit your CV via LinkedIn or message Billy Hall for further information. Aventis Solutions is acting on behalf of our partner.
AI/ML Engineer - Senior, Principal & Team Lead - Team Build employer: Aventis Solutions
Contact Detail:
Aventis Solutions Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land AI/ML Engineer - Senior, Principal & Team Lead - Team Build
✨Tip Number 1
Familiarise yourself with the latest AI/ML frameworks and tools mentioned in the job description, such as PyTorch, TensorFlow, and AWS SageMaker. Being able to discuss your hands-on experience with these technologies during interviews will demonstrate your technical proficiency.
✨Tip Number 2
Showcase your collaborative skills by preparing examples of how you've worked effectively in cross-functional teams. Highlighting your experience in pair programming or agile ceremonies can set you apart as a team player who fits well within their innovative culture.
✨Tip Number 3
Stay updated on the latest trends in AI, especially around LLMs and generative AI. Being able to discuss recent advancements or projects you've worked on in this area can show your passion for innovation and experimentation, which is highly valued by the team.
✨Tip Number 4
Prepare to articulate the real-world impact of your previous AI/ML projects. Understanding how your work has influenced business metrics or user experiences will help you communicate your commercial awareness, making you a more attractive candidate.
We think you need these skills to ace AI/ML Engineer - Senior, Principal & Team Lead - Team Build
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in AI/ML, particularly any work with Python and modern ML frameworks. Emphasise your background in building and deploying models, as well as any exposure to big data systems.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for AI and machine learning. Discuss specific projects or experiences that demonstrate your skills and how they align with the role's requirements. Mention your interest in working on generative AI use cases if applicable.
Showcase Your Technical Skills: In your application, clearly outline your proficiency with tools and technologies mentioned in the job description, such as AWS SageMaker, TensorFlow, and Docker. Provide examples of how you've used these in past projects.
Highlight Collaboration Experience: Since the role requires teamwork, include examples of how you've successfully collaborated with cross-functional teams. Mention any agile methodologies you've worked with and your experience in pair programming or code reviews.
How to prepare for a job interview at Aventis Solutions
✨Showcase Your Technical Skills
Be prepared to discuss your experience with Python and modern ML frameworks like PyTorch or TensorFlow. Highlight specific projects where you've built and deployed machine learning models, especially in production environments.
✨Demonstrate Your Problem-Solving Ability
Expect to face technical challenges during the interview. Be ready to explain your thought process when tackling complex problems and how you approach building reusable ML components.
✨Communicate Clearly
Practice explaining complex AI concepts in simple terms. You may need to present ideas or findings, so being able to communicate effectively with both technical and non-technical audiences is crucial.
✨Emphasise Collaboration
Since the role involves working closely with cross-functional teams, share examples of how you've successfully collaborated with colleagues from different backgrounds, such as product management or DevOps.
