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- Tasks: Join us to design and deploy cutting-edge AI/ML models and solutions.
- Company: Be part of a dynamic team at the forefront of Generative AI and Data Science.
- Benefits: Enjoy hybrid work options, competitive salary, and opportunities for professional growth.
- Why this job: Make a real impact with innovative technology while collaborating with diverse teams.
- Qualifications: 4-5 years in AI/ML or data science, with hands-on experience in GenAI and LLMs.
- Other info: Contact Ben for more details if you're interested in this exciting opportunity!
AI/ML and Data Science Developer
Hybrid – Stevenage 3/4dpw
70,000 – 80,000
About the Role:
We are looking for an AI/ML and Data Science Developer to join a growing team working on cutting-edge Generative AI and Data Science solutions. This role offers the opportunity to design, develop, and deploy advanced AI/Machine Learning models, including Large Language Models (LLMs) and GenAI, while working with diverse datasets and modern deployment technologies.
You\’ll play a key role in data processing, model fine-tuning, and deployment, helping to shape innovative solutions that make a tangible impact.
Key Responsibilities:
- Design, develop, and deploy AI/Machine learning models and solutions, including LLMs and GenAI.
- Fine-tune and evaluate open-source LLMs, applying techniques such as prompt engineering and model re-tuning.
- Work with a variety of structured and unstructured datasets, handling preprocessing, cleaning, and feature engineering.
- Develop pipelines for creating, preparing, and optimising data for modelling.
- Deploy models to production using containerisation tools (Docker, Kubernetes) and ensure scalability, robustness, and monitoring.
- Research and apply the latest advancements in Generative AI and data science.
- Support model evaluation, logging, and performance tuning across GPU-based environments.
- Collaborate with stakeholders to gather requirements and deliver solutions aligned to business needs.
- Document workflows, data pipelines, and model processes for knowledge transfer and reproducibility.
Key Skills & Experience:
- 4-5 years\’ experience across AI/ML, data science, or data engineering, with recent hands-on work in GenAI.
- Proven experience fine-tuning and deploying open-source LLMs.
- Strong knowledge of AI/ML algorithms and techniques (supervised, unsupervised, reinforcement learning).
- Solid background in data preprocessing, wrangling, and feature engineering.
- Proficiency in Python (essential) and familiarity with relevant libraries (NumPy, pandas, scikit-learn, TensorFlow, PyTorch).
- Experience with prompt engineering and model evaluation.
- Deployment experience using Docker or other containerisation tools.
- Exposure to GPU-based environments for large-scale model training and tuning.
- Experience with big data tools (Spark, Hadoop) and cloud platforms (AWS, GCP, Azure) is a plus.
- Strong analytical mindset with the ability to translate data into actionable insights.
If you or someone you know of might be interested, please contact (phone number removed) and ask for Ben.
AI/ML and Data Science Developer employer: Harrington Boyd
Contact Detail:
Harrington Boyd Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land AI/ML and Data Science Developer
✨Tip Number 1
Familiarise yourself with the latest advancements in Generative AI and data science. Follow relevant blogs, attend webinars, or join online communities to stay updated on trends and technologies that are shaping the field.
✨Tip Number 2
Network with professionals in the AI/ML space. Attend industry meetups or conferences where you can connect with potential colleagues and learn about their experiences. This could lead to valuable insights and even referrals.
✨Tip Number 3
Showcase your hands-on experience with open-source LLMs by contributing to projects on platforms like GitHub. This not only demonstrates your skills but also helps you build a portfolio that stands out to hiring managers.
✨Tip Number 4
Prepare for technical interviews by practising coding challenges and system design questions related to AI/ML. Use platforms like LeetCode or HackerRank to sharpen your problem-solving skills and get comfortable with the types of questions you might face.
We think you need these skills to ace AI/ML and Data Science Developer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in AI/ML and data science. Focus on specific projects where you've designed, developed, or deployed models, especially those involving LLMs and GenAI.
Craft a Compelling Cover Letter: In your cover letter, express your passion for AI and data science. Mention specific technologies and methodologies you’ve worked with, such as Docker, Kubernetes, or any big data tools, to demonstrate your fit for the role.
Showcase Your Technical Skills: Include a section in your application that lists your technical skills, particularly in Python and relevant libraries like TensorFlow and PyTorch. Highlight any experience with prompt engineering and model evaluation.
Demonstrate Problem-Solving Abilities: Provide examples of how you've tackled challenges in previous roles, particularly in data preprocessing, feature engineering, or model fine-tuning. This will show your analytical mindset and ability to translate data into actionable insights.
How to prepare for a job interview at Harrington Boyd
✨Showcase Your Technical Skills
Be prepared to discuss your experience with AI/ML algorithms and techniques. Highlight specific projects where you've fine-tuned and deployed open-source LLMs, and be ready to explain the methodologies you used.
✨Demonstrate Problem-Solving Abilities
Expect to face technical challenges during the interview. Practice explaining how you approach data preprocessing, feature engineering, and model evaluation. Use examples from your past work to illustrate your problem-solving skills.
✨Familiarise Yourself with Deployment Tools
Since deployment is a key responsibility, make sure you can discuss your experience with containerisation tools like Docker and Kubernetes. Be ready to explain how you've ensured scalability and robustness in your previous projects.
✨Stay Updated on Industry Trends
Research the latest advancements in Generative AI and data science. Being knowledgeable about current trends will not only impress your interviewers but also show your passion for the field and your commitment to continuous learning.
