

Nexify Infosystems
AI/ML Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for an AI/ML Engineer in London, UK (Hybrid), outside IR35, offering competitive pay. Requires 7+ years in software engineering, strong Python skills, experience with GenAI apps, and proficiency in ML/NLP. Familiarity with cloud platforms and secure deployments is essential.
🌎 - Country
United Kingdom
💱 - Currency
£ GBP
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💰 - Day rate
Unknown
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🗓️ - Date
October 23, 2025
🕒 - Duration
Unknown
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🏝️ - Location
Hybrid
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📄 - Contract
Outside IR35
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🔒 - Security
Unknown
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📍 - Location detailed
London Area, United Kingdom
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🧠 - Skills detailed
#AI (Artificial Intelligence) #Generative Models #Programming #Deployment #NLP (Natural Language Processing) #"ETL (Extract #Transform #Load)" #Data Pipeline #Monitoring #AWS (Amazon Web Services) #Langchain #Model Evaluation #Python #MLflow #Scala #React #ML (Machine Learning) #PyTorch #TensorFlow #Cloud #Observability #FastAPI #HBase #Transformers
Role description
Position: AI/ML Engineer
Location: London, UK (Hybrid Role)
Position Type: Outside IR35
Required Qualifications
• 7+ years in software engineering or applied ML building real-world AI/ML systems; strong Python proficiency and backend development expertise.
• Hands-on experience building GenAI apps with LangChain and LangGraph, including agent design, state/memory management, and graph-based orchestration.
• Proficiency in ML/NLP and generative models; experience with embeddings, vector stores, RAG, and LLM integration/fine-tuning (OpenAI, LLaMA, Cohere, etc.)
• Strong coding in Python and experience with frameworks/tools such as FastAPI, PyTorch/TensorFlow, MLflow; solid understanding of software engineering fundamentals and secure development.
• Experience with AI agent frameworks and MCP; familiarity with agent observability (LangSmith/LangFuse) and agentic RAG patterns
• Track record of delivering scalable, production AI systems and collaborating across teams.
• Experience with agent frameworks (AutoGen, CrewAI), tool-use ecosystems, and advanced planning/reasoning strategies.
• Knowledge of cloud platforms (AWS), MLOps, and data pipelines; React.js familiarity is a plus.
• Exposure to enterprise environments and secure, compliant deployments.
Key Skills
• Programming: Python; backend APIs (FastAPI)
• AI/ML: ML/NLP, generative AI, embeddings, model evaluation
• Frameworks: LangChain, LangGraph; plus LlamaIndex, PyTorch, TensorFlow, MLflow
• Architectures: RAG, Transformers, OCR
• Agents: Design and orchestration, memory/state management, tool integration; MCP and agent-to-agent protocols
• Observability: LangSmith/LangFuse for agent monitoring
Position: AI/ML Engineer
Location: London, UK (Hybrid Role)
Position Type: Outside IR35
Required Qualifications
• 7+ years in software engineering or applied ML building real-world AI/ML systems; strong Python proficiency and backend development expertise.
• Hands-on experience building GenAI apps with LangChain and LangGraph, including agent design, state/memory management, and graph-based orchestration.
• Proficiency in ML/NLP and generative models; experience with embeddings, vector stores, RAG, and LLM integration/fine-tuning (OpenAI, LLaMA, Cohere, etc.)
• Strong coding in Python and experience with frameworks/tools such as FastAPI, PyTorch/TensorFlow, MLflow; solid understanding of software engineering fundamentals and secure development.
• Experience with AI agent frameworks and MCP; familiarity with agent observability (LangSmith/LangFuse) and agentic RAG patterns
• Track record of delivering scalable, production AI systems and collaborating across teams.
• Experience with agent frameworks (AutoGen, CrewAI), tool-use ecosystems, and advanced planning/reasoning strategies.
• Knowledge of cloud platforms (AWS), MLOps, and data pipelines; React.js familiarity is a plus.
• Exposure to enterprise environments and secure, compliant deployments.
Key Skills
• Programming: Python; backend APIs (FastAPI)
• AI/ML: ML/NLP, generative AI, embeddings, model evaluation
• Frameworks: LangChain, LangGraph; plus LlamaIndex, PyTorch, TensorFlow, MLflow
• Architectures: RAG, Transformers, OCR
• Agents: Design and orchestration, memory/state management, tool integration; MCP and agent-to-agent protocols
• Observability: LangSmith/LangFuse for agent monitoring






