At a Glance
- Tasks: Design and develop an AI platform that revolutionises scientific research.
- Company: Join a pioneering tech company transforming scientific discovery with AI.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Collaborative, fast-paced environment with excellent career advancement potential.
- Why this job: Be at the forefront of AI innovation and make a real impact in research.
- Qualifications: 3+ years in ML/AI systems; expertise in Python and LLMs required.
The predicted salary is between 60000 - 80000 £ per year.
Constructor Tech aims to revolutionise scientific discovery by empowering researchers with intelligent, agentic AI assistants. This platform unifies literature mining, knowledge mapping, hypothesis generation, computational experimentation, results analysis, and publication support into a seamless, extensible environment. We are seeking a seasoned ML engineer to help architect and build this next-generation research companion.
As a Machine Learning Engineer on our Agentic AI team, you will design, develop, and deploy the core platform that orchestrates autonomous AI agents and toolchains for diverse scientific workflows. You’ll collaborate closely with research scientists, data engineers, UX designers, and DevOps to turn cutting-edge AI research into production-grade features that accelerate literature review, knowledge graph construction, gap detection, computational modelling, and publication drafting.
Key Responsibilities
- Platform Architecture & Development: Architect and implement a modular, microservices-based agentic AI platform supporting multi-agent orchestration. Develop robust APIs and SDKs enabling seamless integration of AI assistants and external tools (e.g., literature databases, simulation engines).
- AI Agent & Tool Integration: Build and integrate autonomous agents leveraging large language models (LLMs), retrieval-augmented generation, and reinforcement learning for task planning and execution. Incorporate specialized tools for:
- Literature Research: automated document retrieval, semantic search, summarisation.
- Knowledge Mapping: dynamic knowledge graph construction, entity linking, relationship inference.
- Gap Finding & Hypothesis Generation: algorithmic identification of under-explored research areas.
- Computational Research Pipelines: integration with simulation, statistical, and data-analysis tools (e.g., Jupyter, SciPy, custom workflows).
- Results Analysis & Publication: data visualisation modules, automated report and manuscript drafting.
- Model Development & Optimisation: Fine-tune and benchmark LLMs, graph neural networks, and other deep learning architectures for domain-specific tasks. Implement efficient inference pipelines, caching strategies, and batching for real-time interactivity.
- Collaboration & Best Practices: Work in cross-functional Agile teams; participate in design reviews, sprint planning, and code reviews. Ensure high code quality, unit/integration testing, and continuous integration/deployment (CI/CD). Document system designs, APIs, and operational runbooks.
Required Qualifications
- Education: Bachelor’s or Master’s in Computer Science, Machine Learning, AI, physics, chemistry or biology (PhD preferred).
- Experience: 3+ years developing production-scale ML/AI systems, ideally involving agentic or multi-agent frameworks. Proven track record with LLMs (e.g., GPT, T5), RAG architectures, and knowledge graph technologies.
- Technical Skills: Expert in Python; familiarity with Rust, Go, or TypeScript a plus. Frameworks & Libraries: PyTorch or TensorFlow; LangChain, LlamaIndex, Haystack, or similar. Data & Infrastructure: Elasticsearch, Neo4j or other graph databases; Docker, Kubernetes; AWS/GCP/Azure. Tooling: RESTful APIs, gRPC, message queues (e.g., RabbitMQ, Kafka).
- Soft Skills: Strong problem-solving, communication, and collaboration abilities. Comfort working in fast-paced, research-driven environments with evolving requirements.
Preferred Qualifications
- PhD in AI/ML, Computational Science, or a scientific domain (biology, chemistry, physics).
- Experience in academic publishing or research support tools.
- Contributions to open-source AI frameworks or scientific software.
- Familiarity with continuous learning pipelines and MLOps best practices.
AI Engineer, Agentic AI Solutions (Scientific Research Platform) employer: Constructor Group
At Constructor Tech, we are committed to fostering a dynamic and innovative work environment where AI Engineers can thrive. Our collaborative culture encourages continuous learning and professional growth, providing employees with the opportunity to work alongside leading researchers and engineers in the field of AI. Located in a vibrant tech hub, we offer competitive benefits and a unique chance to contribute to groundbreaking advancements in scientific research through our cutting-edge platform.
StudySmarter Expert Advice🤫
We think this is how you could land AI Engineer, Agentic AI Solutions (Scientific Research Platform)
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with professionals on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to AI and machine learning. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and soft skills. Practice common interview questions and be ready to discuss your past experiences and how they relate to the role at Agentic AI Solutions.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining our team at Constructor Tech.
We think you need these skills to ace AI Engineer, Agentic AI Solutions (Scientific Research Platform)
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the AI Engineer role. Highlight your experience with ML systems, especially any work with agentic frameworks or LLMs. We want to see how your skills align with our mission at Constructor Tech!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Share your passion for scientific discovery and how you envision using AI to revolutionise research. Let us know why you're excited about joining our team and what unique perspectives you bring.
Showcase Relevant Projects:Include any relevant projects or contributions in your application. Whether it's a personal project or open-source contributions, we love seeing practical examples of your work with AI and ML technologies. It helps us understand your hands-on experience!
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us that you’re genuinely interested in being part of our innovative team!
How to prepare for a job interview at Constructor Group
✨Know Your Tech Inside Out
Make sure you’re well-versed in the technical skills listed in the job description. Brush up on your Python, and be ready to discuss your experience with frameworks like PyTorch or TensorFlow. They’ll likely ask you about your work with LLMs and multi-agent systems, so have some examples ready to showcase your expertise.
✨Showcase Your Collaboration Skills
Since this role involves working closely with research scientists, data engineers, and UX designers, be prepared to talk about your past collaborative experiences. Share specific instances where you’ve successfully worked in cross-functional teams, highlighting your communication and problem-solving skills.
✨Prepare for Real-World Scenarios
Expect to tackle practical problems during the interview. They might present you with a scenario related to literature mining or knowledge mapping. Think through how you would approach these challenges, and be ready to discuss your thought process and the tools you would use.
✨Demonstrate Your Passion for Research
This role is all about revolutionising scientific discovery, so show your enthusiasm for the field! Talk about any relevant projects you’ve worked on, especially those that involved computational modelling or hypothesis generation. Let them see your genuine interest in advancing research through AI.
