Direct message the job poster from Grosvenor Engineering Group
Recruitment Co-Ordinator at Grosvenor Engineering Group Job Title: Graduate ML/AI Software Engineer
Department: Research & Development
Reports To: Product and Strategy Lead
Summary: We are seeking a motivated and curious Graduate ML/AI Engineer to join our R&D team on an 8-month contract and contribute to the design and prototyping of AI-powered features within our next-generation intelligent asset management platform. This is an exciting opportunity for a recent graduate or early-career engineer eager to work hands-on with Generative AI, automation, and real-world data challenges.
You will work closely with experienced backend engineers, frontend developers, and domain experts to build quick MVPs, explore AI-driven insights, and help bring intelligent features into our product ecosystem.
Responsibilities: Get hands-on with our current AI integrated tools and platform features. Design and develop functional, MVP-ready AI models and services for real-time or scheduled inference. Explore and prototype new AI-driven feature sets, aligned with 4 key product value proposition pillars. Rapidly spin up use-case-specific MVPs for internal validation and client pilots. Contribute to generative AI solutions (LLMs, agents, summarisation, natural language input/output flows). Architecture and Automation: Define scalable AI service architectures and data workflows in collaboration with our R&D team. Build intelligent automation pipelines that translate raw operational data into actionable recommendations. Contribute to defining standards, documentation, and deployment pipelines for productionizing models and microservices. Cross-functional Collaboration: Work alongside software engineers (front-end and backend) and product designers to integrate ML/AI features into the VerdeOS platform. Partner with data analysts, system integrators, and energy engineers to understand field problems and create relevant AI solutions. Co-design insights delivery and feedback capture loops to refine and improve model outcomes. Qualifications Technical Expertise: Recently completed or in the final year of a Bachelor's or Master's degree in Computer Science, AI/ML, Data Science, Engineering, or a related discipline. Strong foundational Python skills and familiarity with ML libraries such as scikit-learn, XGBoost, PyTorch, or TensorFlow. Exposure to deploying ML models or building AI projects during coursework, internships, or personal projects. Interest in learning and working with current-generation AI solutions such as: Embedding models and vector databases (e.g., Pinecone, FAISS, Weaviate) Agent frameworks (e.g., LangChain, Semantic Kernel, Haystack) Hands-on exposure to AWS cloud services (e.g., SageMaker, Lambda, Bedrock, EC2, S3, API Gateway, DynamoDB). Basic understanding of containerization and orchestration tools (e.g., Docker, ECS, EKS). Exposure to CI/CD workflows and infrastructure as code (e.g., CloudFormation, Terraform) is a plus. Awareness of cloud AI tools (AWS SageMaker, Lambda, Bedrock, or similar). Willingness to learn about CI/CD pipelines, MLOps, and real-world signal processing from IoT or industrial systems. Leadership and Communication: Demonstrated ability to communicate and collaborate effectively with cross-disciplinary teams. Capable of balancing fast iteration on MVPs with long-term scalable architectural thinking. Passion for staying current with new developments in AI, ML Ops, and automation in the built environment. Key Performance Indicators (KPIs): MVP Delivery Speed: Time from ideation to internal MVP demonstration (e.g., = 4 weeks). Model Deployment Rate: Number of models deployed as microservices or features per quarter. Automation Coverage: % of asset insights generated through automated AI workflows. Model Performance: Metrics such as accuracy, F1 score, latency, or explainability based on use case. Feature Adoption: Usage rate of AI-driven features in production environments. Seniority level Seniority level Entry level Employment type Employment type Contract Job function Job function Information Technology Industries Facilities Services Referrals increase your chances of interviewing at Grosvenor Engineering Group by 2x
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