Randstad Digital
Machine Learning Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Machine Learning Engineer with a contract length of "unknown" and a pay rate of "unknown." Key skills include Python, SQL, and experience with MLOps, AI design, and data engineering. Familiarity with Kubernetes and cloud services is essential.
🌎 - Country
United Kingdom
💱 - Currency
£ GBP
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💰 - Day rate
Unknown
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🗓️ - Date
May 2, 2026
🕒 - Duration
Unknown
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🏝️ - Location
Unknown
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
London Area, United Kingdom
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🧠 - Skills detailed
#ML (Machine Learning) #Scala #AI (Artificial Intelligence) #Data Engineering #PyTorch #Python #Docker #Kubernetes #GitHub #Langchain #SQL (Structured Query Language) #GCP (Google Cloud Platform) #Transformers #MLflow #AWS (Amazon Web Services) #Azure #Bash #DevOps #Cloud #Airflow #"ETL (Extract #Transform #Load)"
Role description
Currently looking for a ML Engineer
A hands-on role bridging AI research and production. You will design agentic workflows and LLM architectures while building the MLOps "factory" to ensure models are scalable, secure, and monitorable.
Core Responsibilities
•
• AI Design: Build autonomous agents using LangGraph or CrewAI and implement GraphRAG/Agentic RAG pipelines.
• Model Tuning: Fine-tune LLMs using LoRA/QLoRA and optimize inference with vLLM or Quantization.
• Data Engineering: Manage Vector DBs (Pinecone, Milvus) and architect ETL/ELT pipelines via Airflow or AWS.
• MLOps & DevOps: Deploy via Docker/Kubernetes using CI/CD (GitHub Actions, ArgoCD).
• Lifecycle & Safety: Track drift with W&B or MLflow and ensure Responsible AI (bias detection, red teaming).
Technical Profile
• Languages: Python (Expert), SQL, and Bash.
• Frameworks: PyTorch, Transformers, LlamaIndex, and LangChain.
• Infrastructure: Kubernetes, Helm, and Cloud (AWS/GCP/Azure)
• Key Skills: RAG optimization, Multi-agent orchestration, and TDD
Currently looking for a ML Engineer
A hands-on role bridging AI research and production. You will design agentic workflows and LLM architectures while building the MLOps "factory" to ensure models are scalable, secure, and monitorable.
Core Responsibilities
•
• AI Design: Build autonomous agents using LangGraph or CrewAI and implement GraphRAG/Agentic RAG pipelines.
• Model Tuning: Fine-tune LLMs using LoRA/QLoRA and optimize inference with vLLM or Quantization.
• Data Engineering: Manage Vector DBs (Pinecone, Milvus) and architect ETL/ELT pipelines via Airflow or AWS.
• MLOps & DevOps: Deploy via Docker/Kubernetes using CI/CD (GitHub Actions, ArgoCD).
• Lifecycle & Safety: Track drift with W&B or MLflow and ensure Responsible AI (bias detection, red teaming).
Technical Profile
• Languages: Python (Expert), SQL, and Bash.
• Frameworks: PyTorch, Transformers, LlamaIndex, and LangChain.
• Infrastructure: Kubernetes, Helm, and Cloud (AWS/GCP/Azure)
• Key Skills: RAG optimization, Multi-agent orchestration, and TDD
