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Machine Learning Engineer

The Portfolio Group
Posted a day ago, valid for a month
Location

Hinckley, Leicestershire LE10 3FF, England

Contract type

Full Time

In order to submit this application, a Reed account will be created for you. As such, in addition to applying for this job, you will be signed up to all Reed’s services as part of the process. By submitting this application, you agree to Reed’s Terms and Conditions and acknowledge that your personal data will be transferred to Reed and processed by them in accordance with their Privacy Policy.

Sonic Summary

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  • An exceptional opportunity for a Machine Learning Engineer with Full-Stack experience is available at a leading company focused on next-generation digital solutions.
  • The role involves leveraging generative AI, retrieval-augmented generation, and reasoning frameworks to create intelligent systems.
  • Candidates should have 3-5+ years of experience in machine learning and software development, with proficiency in Python and frameworks like PyTorch or TensorFlow.
  • The position offers a competitive salary and benefits package, with responsibilities including model fine-tuning, deployment, and full-stack integration.
  • This office-based role is located in Leicestershire and seeks individuals eager to contribute to AI-driven automation projects.

An exceptional opportunity for a Machine Learning Engineer (with Full-Stack experience) to join an innovative market leader at the forefront of developing next-generation solutions that transform digital interactions. The role will focus on projects to leverage state-of-the-art generative AI, retrieval-augmented generation (RAG), and reasoning frameworks to build intelligent and context-aware systems.

We are seeking talented Machine Learning Engineers with full-stack software development experience to join our client's team and help shape the future of AI-powered automation. Within this dynamic role varied duties will include:

  • Search relevancy engineering.
  • Conversational AI Development: Design, train, fine-tune, and deploy LLMs with reasoning capabilities.
  • Retrieval-Augmented Generation (RAG): Implement, optimise, and scale RAG pipelines for effective information retrieval from structured and unstructured sources.
  • Model Fine-Tuning & Training: Train domain-specific models using techniques like LoRA, QLoRA, PEFT, reinforcement learning, and supervised fine-tuning (SFT).
  • Model Deployment & Inferencing: Optimise model serving and inference using vLLM, DeepSpeed, TensorRT, Triton, and other acceleration frameworks.
  • Multi-Agent Systems: Develop and integrate agentic capabilities using frameworks such as LangChain, CrewAI, AutoGen, and DSPy.
  • AWS Cloud & MLOps: Deploy scalable machine learning workloads on AWS using services like SageMaker, Bedrock, Lambda, S3, DynamoDB, ECS, and EKS.
  • End-to-End AI Product Development: Work across the full ML lifecycle, from data collection and preprocessing to model evaluation, deployment, and monitoring.
  • Full-Stack Integration: Develop APIs and integrate ML models into web applications using FastAPI, Flask, React, TypeScript, and Node.js.
  • Vector Databases & Search: Implement embeddings and retrieval mechanisms using Pinecone, Weaviate, FAISS, Milvus, ChromaDB, or OpenSearch.

Required skills & experience:

  • 3-5+ years in machine learning and software development
  • Proficient in Python, PyTorch or TensorFlow or Hugging Face Transformers
  • Experience with RAG, LLM fine-tuning, and expertise in AWS and cloud-native AI deployments.
  • Full-stack experience (React, TypeScript, Node.js) and API development.
  • Familiarity with vector search and multi-agent orchestration

Apply now to join this high growth and award-winning organisation with the opportunity to be part of building the future of AI driven projects and solutions. The role offers a highly competitive salary and benefits package and will be office based in Leicestershire.

MLE020525AM

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In order to submit this application, a Reed account will be created for you. As such, in addition to applying for this job, you will be signed up to all Reed’s services as part of the process. By submitting this application, you agree to Reed’s Terms and Conditions and acknowledge that your personal data will be transferred to Reed and processed by them in accordance with their Privacy Policy.