12 of 12 Machine Learning Jobs in Midlothian

higher-level apprenticeship programmes such as Advanced Data Fellowship and AI Data Specialist. You will cover a range of topics, including: Fundamentals of ML and AI (including Machine Learning Lifecycle, Machine Learning Mathematical Foundations); Solving Business Problems with Machine Learning; Data Preparation and Feature … Engineering; Planning and preparing ML/AI solution; Data Ethics and Responsible AI; Model Engineering and Training; Model Evaluation and Deployment; Monitoring, Maintenance, and Continuous Learning; Data Security, Privacy, and Governance; Stakeholder Communication and Documentation The Multiverse coaching team is a diverse mix of industry experts, educators, and academics ...

Project Work with Hugging Face to build a sentiment analysis classifier using real-world AI techniques. Step 8 – AI Prompt Engineering Master prompt engineering, learning how to craft effective prompts for controlling AI outputs. Step 9 – Retrieval-Augmented Generation (RAG) Learn how to integrate external knowledge into AI systems … Combine prompt engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases. Step 11 – Machine Learning Fundamentals Understand machine learning principles and algorithms, and how to train and test models using scikit-learn. Step 12 – Machine ...

Project Work with Hugging Face to build a sentiment analysis classifier using real-world AI techniques. Step 8 – AI Prompt Engineering Master prompt engineering, learning how to craft effective prompts for controlling AI outputs. Step 9 – Retrieval-Augmented Generation (RAG) Learn how to integrate external knowledge into AI systems … Combine prompt engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases. Step 11 – Machine Learning Fundamentals Understand machine learning principles and algorithms, and how to train and test models using scikit-learn. Step 12 – Machine ...

technical execution, ensuring compliance with data governance, security, and ethical AI standards. Key Responsibilities: Strategy & Design: Define end-to-end architectures for AI, ML, and data solutions on Google Cloud Platform (GCP) while ensuring alignment to platform strategy and security controls. Technical Leadership: Deliver high-quality solutions using GCP cloud … Learning, and AI tools. Experience with large-scale data migrations is a plus! Architecture Leadership: Ability to design orchestrations for AI and ML systems, articulating trade-offs and leading design decisions. Communication Skills: Exceptional ability to convey complex technical concepts and represent the lab's technical direction ...

Lead ML Engineer (12-month FTC) £80,000 - £90,000 Remote (UK Based Only) This is a rare opportunity to step into a senior, hands-on ML Engineering role where you will shape how machine learning is delivered, scaled, and embedded across a large, complex organisation. THE COMPANY … strategic priority, with significant internal backing to move from successful proof of concepts to robust, production-grade platforms. THE ROLE As a Lead ML Engineer, you will take ownership of ML Engineering delivery end-to-end, helping define best practice while remaining highly hands-on. Specifically, you can expect ...

cutting-edge AI technologies, including LLMs and agentic frameworks* Work across a broad range of industries and enterprise-scale projects* Strong focus on learning, innovation, and career progression* Collaborative, people-first culture THE PRINCIPAL AI CONSULTANT ROLE You will act as both a technical leader and trusted advisor, helping … senior business and technical stakeholders* Proven experience delivering AI solutions into production* Strong Python/software engineering background* Experience with Azure AI Foundry, Azure ML, Databricks, AWS SageMaker, Bedrock, or similar platforms* Strong understanding of MLOps, CI/CD, observability, and production AI delivery* Experience leading engineering teams and mentoring ...

Service Bus, Postgres, Key Vault, etc.) Observability tooling (metrics, logs, alerting) Experience supporting services end-to-end in production Familiarity working alongside AI/ML workloads (non-data science focus) Desirable Temporal.io or similar workflow tools LLM serving (e.g. vLLM) or GPU workloads on Kubernetes ArgoCD, KEDA, service mesh (Istio ...

through to being qualified to work in a junior Data Scientist role. Through the interactive courses, you will gain knowledge in Python, R, Machine Learning, AI , and much more. You will also complete mini projects to gain practical experience and test your skills while you study. Step ...

design Senior-level experience as a Data Architect, Principal Engineer or Lead Consultant Strong understanding of modern data platforms (lakehouse, streaming, analytics, AI/ML enablement) Confident communicator, comfortable engaging senior stakeholders and guiding buying decisions A solutions mindset - hands-on enough to be credible, strategic enough to lead What ...

backend, platform, full stack, and data disciplines. Act as the primary engineering leader, allowing the wider technology leadership to focus on data science and machine learning innovation. Set clear standards for engineering best practice, architecture, delivery, and technical decision making. Coordinate and oversee multiple workstreams, ensuring projects ...

Solution Architects through to senior technical leadership. What you'll be doing Designing AI and GenAI architecture strategies and roadmaps Building scalable AI/ML and Agentic AI platforms Working across LLMs, RAG, embeddings and semantic search Supporting AI deployment, governance and integration patterns Collaborating with engineering, data, DevOps … frameworks Vector databases and semantic search MLOps and LLMOps AWS, Azure, GCP, Databricks Containerisation, Kubernetes and GPU infrastructure Requirements Experience designing enterprise AI or ML architectures Strong understanding of cloud and modern data platforms Exposure to scalable AI deployment and integration patterns Financial Services or regulated industry experience essential Strong ...

business outcomes. Key responsibilities: Translating business objectives into AI architecture strategies, roadmaps and scalable solution designs Designing and implementing end to end AI and ML architectures across enterprise environments Defining scalable, performant and cost optimised deployment patterns across cloud, containerised and GPU enabled environments Supporting AI implementation programmes from proof … transformation programmes Producing solution design documentation, implementation plans and technical proposals Providing technical leadership and mentoring within multidisciplinary teams Experience Required: Experience designing AI, ML or modern data architectures within enterprise environments Strong understanding of cloud platforms such as AWS, Azure, GCP, Databricks or similar technologies Experience architecting scalable ...