26 to 50 of 181 LangGraph Jobs in London

code, not just scripts (OOP, async, packaging, testing)Generative AI & LLM development — prompt engineering, fine-tuning, RAG pipelines, context window managementAgentic AI frameworks — LangChain, LangGraph, AutoGen, or similar; building multi-step autonomous workflowsMachine Learning — model development, training, evaluation, and deploymentAPIs & backend development — FastAPI, REST APIs, microservices architectureVector databases & embeddingsCloud platforms ...

with engineering, data, DevOps and business teams to deliver end-to-end solutions.**Tech environment**Python, LLMs (prompt engineering, fine-tuning, RAG), LangChain/LangGraph, agent frameworks, vector databases, MLOps/LLMOps, AWS, Azure, GCP, Databricks, Kubernetes and GPU infrastructure.**What we're looking for**Proven enterprise ...

validate, evaluate, and improve AI-generated outputs; understanding of AI limitations and responsible use Familiarity with agentic system concepts — awareness of orchestration frameworks (LangChain, LangGraph, or equivalent), RAG pipelines, and how full-stack applications connect to agent-based architecture; production experience preferred, conceptual understanding required ...

enterprise environments Some experience working within engineering teams in agile, client-facing delivery contexts AI, data and technology awareness Practical experience with: Agentic frameworks: LangGraph, LangChain, CrewAI, Agentforce or similar Foundation model providers: Claude Code/Codex/Gemini, OpenAI or AWS Bedrock Vector databases: Pinecone, Weaviate, LlamaIndex or pgvector ...

capabilities on cloud platforms — such as Google Vertex AI Agent Builder, AWS Bedrock, or Azure OpenAIFamiliarity with agentic AI frameworks and tooling — such as LangGraph, LangChain, LlamaIndex, or CrewAI — and how they are appliedExperience with retrieval-augmented generation (RAG) architectures, vector databases, and LLM-powered applications as part ...

similar tools. Experience evaluating and adopting emerging technologies, including build-vs-buy decisions. Hands-on experience building AI agents using frameworks such as LangGraph, LangChain, or LlamaIndex. Experience implementing Retrieval-Augmented Generation (RAG) solutions and governance controls for enterprise AI applications. Experience using agentic AI to automate engineering workflows such ...

models behave in production, not just in demos. Agentic Systems Design: Proven experience designing and deploying multi‐agent architectures and orchestration frameworks (e.g., LangGraph, AutoGen, CrewAI); tool‐use patterns, human‐in‐the‐loop workflows, and agentic safety at enterprise scale. AI/ML Engineering & MLOps: Full AI/ML lifecycle ...

enterprise environments Some experience working within engineering teams in agile, client‐facing delivery contexts AI, data and technology awareness Practical experience with: Agentic frameworks: LangGraph, LangChain, CrewAI, Agentforce or similar Foundation model providers: Claude Code/Codex/Gemini, OpenAI or AWS Bedrock Vector databases: Pinecone, Weaviate, LlamaIndex or pgvector ...

environments Proven ability to lead engineering teams in agile, client-facing delivery contexts AI, data and technology awareness Deep practical experience with: Agentic frameworks: LangGraph, LangChain, CrewAI, Agentforce or similar Foundation model providers: Claude Code/Codex/Gemini, OpenAI or AWS Bedrock Vector databases: Pinecone, Weaviate, LlamaIndex or pgvector ...

models behave in production, not just in demos.* Agentic Systems Design:Proven experience designing and deploying multi-agent architectures and orchestration frameworks (e.g., LangGraph, AutoGen, CrewAI); tool-use patterns, human-in-the-loop workflows, and agentic safety at enterprise scale* AI/ML Engineering & MLOps: Full AI/ML lifecycle ...

understand how models behave in production, not just in demos.Agentic Systems Design:Proven experience designing and deploying multi-agent architectures and orchestration frameworks (e.g., LangGraph, AutoGen, CrewAI); tool-use patterns, human-in-the-loop workflows, and agentic safety at enterprise scaleAI/ML Engineering & MLOps: Full AI/ML lifecycle ...

resource optimisation highly desirable.Expert-level proficiency in Python, and modern AI/ML frameworks, including PyTorch, TensorFlow, and specialised Generative AI libraries (LangChain, LangGraph or related open-source toolkits strongly preferred. Background in Traditional ML/AI.Deep understanding of LLMs, prompt engineering, RAG pipelines, vector databases, and generative architectures; related ...

Vertex AI).Generative AI: LLM fine-tuning (LoRA/QLoRA), RAG architecture design, vector databases (Pinecone, Milvus, Weaviate), and agentic frameworks (LangChain, LangGraph, Semantic Kernel).Data pipelines: data lake/lakehouse architectures, ETL/ELT, and data quality/lineage tooling that feed AI systems.Automation, Orchestration & ObservabilityInfrastructure-as-code: Terraform ...

Building, optimising and evaluating Retrieval-Augmented Generation (RAG) solutions using prompt engineering and embedding models. Designing and deploying AI agents using frameworks including LangChain, LangGraph and AutoGen. Delivering production-ready GenAI applications for commercial teams within Trading & Supply. Building scalable AI workflows on Databricks using Mosaic AI, MLflow, Genie ...

Building, optimising and evaluating Retrieval-Augmented Generation (RAG) solutions using prompt engineering and embedding models. Designing and deploying AI agents using frameworks including LangChain, LangGraph and AutoGen. Delivering production-ready GenAI applications for commercial teams within Trading & Supply. Building scalable AI workflows on Databricks using Mosaic AI, MLflow, Genie ...

Building, optimising and evaluating Retrieval-Augmented Generation (RAG) solutions using prompt engineering and embedding models. Designing and deploying AI agents using frameworks including LangChain, LangGraph and AutoGen. Delivering production-ready GenAI applications for commercial teams within Trading & Supply. Building scalable AI workflows on Databricks using Mosaic AI, MLflow, Genie ...

Building, optimising and evaluating Retrieval-Augmented Generation (RAG) solutions using prompt engineering and embedding models. Designing and deploying AI agents using frameworks including LangChain, LangGraph and AutoGen. Delivering production-ready GenAI applications for commercial teams within Trading & Supply. Building scalable AI workflows on Databricks using Mosaic AI, MLflow, Genie ...

Version Control (DVC). Experience developing LLM or Generative AI applications, including RAG architectures and prompt engineering. Knowledge of LLMOps frameworks such as LangChain, LangGraph, or LangSmith. Experience taking ML models from experimentation into production environments. Strong communication skills with the ability to explain technical concepts to both technical ...

Practical experience building LLM/GenAI applications, including prompt engineering and retrieval-augmented generation (RAG) Familiarity with LLMOps frameworks such as LangChain, LangSmith or LangGraph Understanding of model validation, evaluation techniques and production monitoring Experience working in cross-functional teams from problem definition through to delivery Strong communication skills, with ...

Ericsson automation-related certifications.TM Forum Open API certification.Nice-to-Have QualificationsExperience with Google Vertex AI, Gemini APIs, BigQuery, or similar platforms.Experience with LangChain, LangGraph, MCP, or agentic AI execution frameworks.Experience with ServiceNow, Netcracker, Amdocs, Ericsson, Nokia, Cisco NSO, Juniper Paragon, or similar orchestration platforms.Experience building secure automation for production telecom ...

loops, tool-use, planning, and memory patterns. Hands-on experience (or strong aptitude to learn) building or integrating agents using frameworks such as LangChain, LangGraph, AutoGen, CrewAI , or the Model Context Protocol (MCP) . Ability to design and expose backend services as tools/functions callable by AI agents (tool ...

with provenance tracking, including web automation, parsing, and document processing Agent frameworks - hands‐on experience with multi‐agent orchestration tools (e.g., Google ADK, StrandsAgents, LangGraph, CrewAI, or equivalents) Real world data sources – HER, claims, prescriptions, registries - and their pitfalls. AI-augmented development - effective use of agentic coding assistants (Copilot, Cursor ...

software engineering experience, with at least 1-2 years of meaningful production work building LLM-powered features.Direct experience with LLM frameworks such as LangChain, LangGraph, or similar.Hands-on experience building agentic systems: tool use, multi-step reasoning, planning, memory, and human-in-the-loop patterns.Practical understanding of prompt engineering, structured ...

multiple teams Strong experience with cloud platforms (AWS, Azure, GCP) Experience with ML frameworks (PyTorch, TensorFlow, scikit-learn) and Gen AI/Agentic frameworks (LangGraph, LangChain, VectorDBs, RAG) Understanding of responsible AI practices, including guardrails, hallucination mitigation, and output quality management for AI systems Experience designing and scaling low-latency ...

Vertex AI). Generative AI: LLM fine-tuning (LoRA/QLoRA), RAG architecture design, vector databases (Pinecone, Milvus, Weaviate), and agentic frameworks (LangChain, LangGraph, Semantic Kernel). Data pipelines: data lake/lakehouse architectures, ETL/ELT, and data quality/lineage tooling that feed AI systems. Automation, Orchestration & Observability ...