

Randstad Digital
Machine Learning Engineer
β - Featured Role | Apply direct with Data Freelance Hub
This role is for a Lead Machine Learning Engineer focusing on Generative AI and Agentic Systems, with a 12-month contract in London (hybrid). Key skills include LLM fine-tuning, MLOps, and data orchestration.
π - Country
United Kingdom
π± - Currency
Β£ GBP
-
π° - Day rate
Unknown
-
ποΈ - Date
May 7, 2026
π - Duration
More than 6 months
-
ποΈ - Location
Hybrid
-
π - Contract
Fixed Term
-
π - Security
Unknown
-
π - Location detailed
London Area, United Kingdom
-
π§ - Skills detailed
#Deployment #Predictive Modeling #Docker #Scala #Data Science #Databases #MLflow #AI (Artificial Intelligence) #Data Orchestration #Automation #Documentation #ML (Machine Learning) #"ETL (Extract #Transform #Load)" #Kubernetes #Compliance #Data Governance
Role description
Lead Machine Learning Engineer (Generative AI & Agentic Systems)
Location: London, UK (Hybrid: 2 days/week in-office)
Type: 12-Month Contract
The Opportunity
Are you a hands-on leader in the AI space? We are looking for a Lead ML Engineer to spearhead the design, deployment, and optimization of sophisticated AI models and Agentic Systems.
This isn't just about standard predictive modelingβyouβll be building autonomous agents that reason and execute, leveraging the latest in LLM fine-tuning, RAG pipelines, and scalable MLOps.
The Core Mission
β’ Architect & Build: Design and implement AI algorithms and architectures, moving from raw concepts to robust frameworks.
β’ Agentic Systems & LLMs: Develop intelligent AI agents capable of reasoning and planning. Expertly handle LLM fine-tuning (PEFT, LoRA, QLoRA) and RAG pipelines.
β’ Data Orchestration: Build ETL/ELT pipelines and feature engineering workflows to integrate structured and unstructured data into centralized platforms.
β’ End-to-End MLOps: Own the lifecycleβfrom CI/CD automation and containerization (Docker/Kubernetes) to versioning and infrastructure management.
β’ Responsible AI: Ensure every system is trustworthy, fair, and explainable, implementing quantifiable metrics for bias detection and regulatory compliance.
Technical Toolkit
β’ Models: LLMs, Generative AI, Agentic workflows.
β’ Engineering: PEFT, Vector Databases (Pinecone/Milvus/Weaviate), Prompt Engineering.
β’ Ops: Docker, Kubernetes, CI/CD, Experiment Tracking (MLflow/W&B).
β’ Data: ETL/ELT, Feature Stores, Performance Tuning.
Who You Are
β’ A Technical Lead: You can bridge the gap between Data Science, Software Engineering, and the business.
β’ A Precision Engineer: You value documentation, data governance, and "bulletproof" deployment.
β’ A Strategic Thinker: You donβt just build; you optimize for scalability, performance, and cost-efficiency.
Logistics
β’ Contract: 12-month initial term.
β’ Location: London-based office. Candidates must be able to commute to the office 2 days per week (mandatory).
Are you ready to build the next generation of autonomous AI?
#MLEngineer #GenAI #LLM #MachineLearning #AgenticSystems #MLOps #LondonJobs #ContractHiring #DataScience #ArtificialIntelligence #AIJobs
Lead Machine Learning Engineer (Generative AI & Agentic Systems)
Location: London, UK (Hybrid: 2 days/week in-office)
Type: 12-Month Contract
The Opportunity
Are you a hands-on leader in the AI space? We are looking for a Lead ML Engineer to spearhead the design, deployment, and optimization of sophisticated AI models and Agentic Systems.
This isn't just about standard predictive modelingβyouβll be building autonomous agents that reason and execute, leveraging the latest in LLM fine-tuning, RAG pipelines, and scalable MLOps.
The Core Mission
β’ Architect & Build: Design and implement AI algorithms and architectures, moving from raw concepts to robust frameworks.
β’ Agentic Systems & LLMs: Develop intelligent AI agents capable of reasoning and planning. Expertly handle LLM fine-tuning (PEFT, LoRA, QLoRA) and RAG pipelines.
β’ Data Orchestration: Build ETL/ELT pipelines and feature engineering workflows to integrate structured and unstructured data into centralized platforms.
β’ End-to-End MLOps: Own the lifecycleβfrom CI/CD automation and containerization (Docker/Kubernetes) to versioning and infrastructure management.
β’ Responsible AI: Ensure every system is trustworthy, fair, and explainable, implementing quantifiable metrics for bias detection and regulatory compliance.
Technical Toolkit
β’ Models: LLMs, Generative AI, Agentic workflows.
β’ Engineering: PEFT, Vector Databases (Pinecone/Milvus/Weaviate), Prompt Engineering.
β’ Ops: Docker, Kubernetes, CI/CD, Experiment Tracking (MLflow/W&B).
β’ Data: ETL/ELT, Feature Stores, Performance Tuning.
Who You Are
β’ A Technical Lead: You can bridge the gap between Data Science, Software Engineering, and the business.
β’ A Precision Engineer: You value documentation, data governance, and "bulletproof" deployment.
β’ A Strategic Thinker: You donβt just build; you optimize for scalability, performance, and cost-efficiency.
Logistics
β’ Contract: 12-month initial term.
β’ Location: London-based office. Candidates must be able to commute to the office 2 days per week (mandatory).
Are you ready to build the next generation of autonomous AI?
#MLEngineer #GenAI #LLM #MachineLearning #AgenticSystems #MLOps #LondonJobs #ContractHiring #DataScience #ArtificialIntelligence #AIJobs






