1 to 25 of 46 Permanent Machine Learning Jobs in Essex

robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Design and analysis of A/B tests and offline experiments … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Strong proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch). . Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving ...

robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Design and analysis of A/B tests and offline experiments … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Strong proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch). . Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving ...

robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Design and analysis of A/B tests and offline experiments … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Strong proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch). . Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving ...

robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Design and analysis of A/B tests and offline experiments … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Strong proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch). . Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving ...

robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Design and analysis of A/B tests and offline experiments … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Strong proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch). . Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving ...

robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Design and analysis of A/B tests and offline experiments … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Strong proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch). . Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving ...

robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Design and analysis of A/B tests and offline experiments … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Strong proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch). . Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving ...

robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Design and analysis of A/B tests and offline experiments … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Strong proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch). . Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving ...

robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Design and analysis of A/B tests and offline experiments … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Strong proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch). . Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving ...

robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Design and analysis of A/B tests and offline experiments … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Strong proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch). . Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving ...

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 ...

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 ...

Title: AI/ML Engineer – Data Science Location: United Kingdom (Remote/Hybrid) Eligibility: Permanent UK Citizenship required Experience: 5–7 years (Mandatory in Data Science/AI-ML) Role Overview We are seeking a skilled AI/ML Engineer – Data Science to design, develop, and deploy scalable machine … Responsibilities - Design, build, and optimize machine learning and deep learning models for real-world applications. - Develop and deploy AI/ML solutions using modern Python-based frameworks. - Implement NLP models and solutions for structured and unstructured data. - Build and maintain production-ready ML pipelines, ensuring scalability ...

Scrum/agile process Participates in on-call support with the rest of the team What you'll bring Extensive e xperience with traditional ML system s and GenAI ( LLMs, agent frameworks ), including proven delivery of GenAI powered search or chatbot solutions in production Working experience and in-depth understanding … Large Language Models (LLMs) - Vector databases and semantic search - Retrieval-Augmented Generation (RAG) Extensive programming expertise , primarily in Python, delivering ML and GenAI solutions with a language-agnostic mindset Familiar with cloud services (e.g. AWS Lambda best practices) Strong team player and confident communicator with experience as a tech lead ...

Automating Machine Learning workflows (training → deployment) with AWS & GitOps Deploying LLMs using Kubernetes & Docker Building infrastructure with Terraform & Helm Monitoring and maintaining ML models with performance alerts and dashboards Supporting CI/CD for ML pipelines Developing production-grade APIs (REST/gRPC) to serve models Collaborating with … DevOps or MLOps roles (ideally in AWS environments) Hands-on with Docker, Kubernetes, and Terraform Strong scripting skills in Python or Bash Familiar with ML lifecycle tools, model monitoring, and versioning Exposure to tools like KServe, Ray Serve, Triton, or vLLM a big plus Bonus Points: Experience with observability frameworks ...

Scrum/agile process Participates in on-call support with the rest of the team What you'll bring Extensive e xperience with traditional ML system s and GenAI ( LLMs, agent frameworks ), including proven delivery of GenAI powered search or chatbot solutions in production Working experience and in-depth understanding … Large Language Models (LLMs) - Vector databases and semantic search - Retrieval-Augmented Generation (RAG) Extensive programming expertise , primarily in Python, delivering ML and GenAI solutions with a language-agnostic mindset Familiar with cloud services (e.g. AWS Lambda best practices) Strong team player and confident communicator with experience as a tech lead ...

Scrum/agile process Participates in on-call support with the rest of the team What you'll bring Extensive e xperience with traditional ML system s and GenAI ( LLMs, agent frameworks ), including proven delivery of GenAI powered search or chatbot solutions in production Working experience and in-depth understanding … Large Language Models (LLMs) - Vector databases and semantic search - Retrieval-Augmented Generation (RAG) Extensive programming expertise , primarily in Python, delivering ML and GenAI solutions with a language-agnostic mindset Familiar with cloud services (e.g. AWS Lambda best practices) Strong team player and confident communicator with experience as a tech lead ...

Scrum/agile process Participates in on-call support with the rest of the team What you'll bring Extensive e xperience with traditional ML system s and GenAI ( LLMs, agent frameworks ), including proven delivery of GenAI powered search or chatbot solutions in production Working experience and in-depth understanding … Large Language Models (LLMs) - Vector databases and semantic search - Retrieval-Augmented Generation (RAG) Extensive programming expertise , primarily in Python, delivering ML and GenAI solutions with a language-agnostic mindset Familiar with cloud services (e.g. AWS Lambda best practices) Strong team player and confident communicator with experience as a tech lead ...

Scrum/agile process Participates in on-call support with the rest of the team What you'll bring Extensive e xperience with traditional ML system s and GenAI ( LLMs, agent frameworks ), including proven delivery of GenAI powered search or chatbot solutions in production Working experience and in-depth understanding … Large Language Models (LLMs) - Vector databases and semantic search - Retrieval-Augmented Generation (RAG) Extensive programming expertise , primarily in Python, delivering ML and GenAI solutions with a language-agnostic mindset Familiar with cloud services (e.g. AWS Lambda best practices) Strong team player and confident communicator with experience as a tech lead ...

Scrum/agile process Participates in on-call support with the rest of the team What you'll bring Extensive e xperience with traditional ML system s and GenAI ( LLMs, agent frameworks ), including proven delivery of GenAI powered search or chatbot solutions in production Working experience and in-depth understanding … Large Language Models (LLMs) - Vector databases and semantic search - Retrieval-Augmented Generation (RAG) Extensive programming expertise , primarily in Python, delivering ML and GenAI solutions with a language-agnostic mindset Familiar with cloud services (e.g. AWS Lambda best practices) Strong team player and confident communicator with experience as a tech lead ...

Scrum/agile process Participates in on-call support with the rest of the team What you'll bring Extensive e xperience with traditional ML system s and GenAI ( LLMs, agent frameworks ), including proven delivery of GenAI powered search or chatbot solutions in production Working experience and in-depth understanding … Large Language Models (LLMs) - Vector databases and semantic search - Retrieval-Augmented Generation (RAG) Extensive programming expertise , primarily in Python, delivering ML and GenAI solutions with a language-agnostic mindset Familiar with cloud services (e.g. AWS Lambda best practices) Strong team player and confident communicator with experience as a tech lead ...

exceptional tech professionals for this role. As a Lead AI Engineer at Kainos, you will deliver advanced AI solutions leveraging state-of-the-art machine learning, generative and agentic AI technologies. You will drive the adoption of modern AI frameworks, AIOps best practices and scalable cloud-native architectures. … colleagues and more junior developers, providing direction support as you solve challenging problems together. Minimum (essential) Requirements Demonstrable experience of deploying modern AI/ML solutions into production, including prompt engineering, retrieval-augmented generation (RAG), model evaluation, and monitoring using metrics (e.g. precision, recall, NDCG and drift detection). Strong ...

engineering, model integration, RAG patterns, agentic workflows, and guardrails. Ability to define and measure quality, accuracy, and observability metrics for GenAI features in production. • Machine Learning & Data Science Knowledge: Solid understanding of machine learning concepts, model deployment, and inference APIs. Ability to evaluate model accuracy … manager, ideally leading cross functional teams in a SaaS environment. Track record of delivering complex software projects on time, including customer facing GenAI or ML powered features. • Agile Expertise: Deep understanding of Agile principles and Scrum methodology. Scrum Master or similar certification is a plus. • Cross Team Collaboration: Proven experience ...