26 to 50 of 143 LangChain Jobs

integrations Collaborating across engineering, architecture and business teams Supporting AI deployment, governance and operational rollout Environment Python, SQL LLMs, prompt engineering, fine-tuning, RAG LangChain, LangGraph, Agent frameworks Vector databases, FastAPI, APIs AWS, Azure, GCP or Databricks CI/CD, MLOps and LLMOps Requirements Strong software or data engineering foundations ...

with engineering, data, DevOps and business teams Advising clients on AI platform capability and implementation strategy Environment Python LLMs, prompt engineering, fine-tuning, RAG LangChain, LangGraph, Agent frameworks Vector databases and semantic search MLOps and LLMOps AWS, Azure, GCP, Databricks Containerisation, Kubernetes and GPU infrastructure Requirements Experience designing enterprise ...

with engineering, data, DevOps and business teams Advising clients on AI platform capability and implementation strategy Environment Python LLMs, prompt engineering, fine-tuning, RAG LangChain, LangGraph, Agent frameworks Vector databases and semantic search MLOps and LLMOps AWS, Azure, GCP, Databricks Containerisation, Kubernetes and GPU infrastructure Requirements Experience designing enterprise ...

with engineering, data, DevOps and business teams Advising clients on AI platform capability and implementation strategy Environment Python LLMs, prompt engineering, fine-tuning, RAG LangChain, LangGraph, Agent frameworks Vector databases and semantic search MLOps and LLMOps AWS, Azure, GCP, Databricks Containerisation, Kubernetes and GPU infrastructure Requirements Experience designing enterprise ...

Generative AI & LLMs, RAG (Retrieval-Augmented Generation), Agentic AI frameworks Hands-on with; LLMs (OpenAI, Llama, Claude, Mistral), Vector DBs (Pinecone, Weaviate, FAISS), Frameworks (LangChain, LlamaIndex, AutoGen, CrewAI, LangGraph) Strong Python + ML frameworks (PyTorch/TensorFlow) Experience deploying on AWS, Azure, or GCP Experience leading or influencing technical teams ...

development LLM integration experience – OpenAI, Anthropic, or equivalent APIs MCP, REST APIs, Microservices Architecture Cloud platforms – AWS, Azure, or GCP Preferred Agentic framework experience – LangChain, LangGraph, AutoGen, or similar RAG pipeline design and vector database experience (Pinecone, Weaviate, pgvector) Agile/SDLC tooling – Jira, Git, CI/CD pipelines #J ...

workloads inside Kubernetes . Familiarity with Helm charts, K8s networking, storage volumes, and GPU orchestration is essential. GenAI Orchestration: Deep, hands-on experience using LangChain and LangGraph to build complex, stateful, and cyclic agent architectures. Core Tech Stack: Strong proficiency in Python, framework integrations (LlamaIndex, Hugging Face), and vector databases ...

scalability, and system design Help establish engineering best practices in a fast-moving early-stage environment Tech Stack Python TensorFlow/PyTorch LLM frameworks (LangChain, LangGraph, or similar agentic AI tooling) AWS/Azure cloud platforms (including AI services such as Bedrock) Data engineering tools (e.g. AWS Glue or equivalent ...

scalable and responsible AI solutions on cloud platforms (e.g. AWS, Google Cloud, Azure, or equivalent private cloud) 2+ years of experience supporting Agentic Frameworks (LangChain, CrewAI, Semantic Kernel (Microsoft), or AutoGen) 2+ years of experience with LLMOps (Google Cloud Vertex AI, Amazon SageMaker, Azure Machine Learning) 8+ years of experience ...

scalable and responsible AI solutions on cloud platforms (e.g. AWS, Google Cloud, Azure, or equivalent private cloud) 2+ years of experience supporting Agentic Frameworks (LangChain, CrewAI, Semantic Kernel (Microsoft), or AutoGen) 2+ years of experience with LLMOps (Google Cloud Vertex AI, Amazon SageMaker, Azure Machine Learning) 8+ years of experience ...

testing, deployment, monitoring, and optimisation Tech environment Key technologies include: Python Azure Databricks Generative AI/LLM frameworks Agentic AI tooling and orchestration frameworks LangChain/LangGraph PyTorch and ML libraries REST APIs Docker and containerisation CI/CD pipelines Git-based development workflows What we're looking ...

developing and deploying machine learning and generative AI solutions in production environments. * Strong Python engineering skills with experience using frameworks such as PyTorch, TensorFlow, LangChain, or similar. * Experience working with LLMs, prompt engineering, vector databases, and retrieval augmented generation (RAG) architectures. * Strong knowledge of cloud platforms including AWS or Azure. ...

cloud platforms (AWS, GCP, Azure) and tooling (Docker, Kubernetes, Terraform, Helm).* Familiarity with frameworks such as **Spring (AI/Boot/Core), Flask, LangChain, LangGraph, ADK**, MLFlow, and orchestrators like **N8N**.* Experience with **React** and modern UI frameworks (Gradio, Streamlit).* Understanding of **REST/WebSocket APIs ...

cloud platforms (Azure, AWS, or GCP) Experience handling and processing large datasets Knowledge of prompt engineering, fine-tuning, or LLM orchestration frameworks (e.g. LangChain) Strong problem-solving abilities and a passion for innovation Ability to communicate technical concepts to non-technical stakeholders Ability to work independently and as part ...

distinct advantage. Extensive hands‐on experience with Generative AI concepts, Large Language Models (LLMs), transformer architectures, RAG, and advanced agentic frameworks (e.g., LangChain, LangGraph, Google ADK). Optionally AutoGen, CrewAI, LlamaIndex, Semantic Kernel. Deep comfort and practical experience with containers and orchestration technologies, specifically OpenShift. Demonstrated ability to architect, develop ...

enabled environments Experience with LLMs, prompt engineering, embeddings, semantic search and RAG patterns Exposure to vector databases and agent frameworks such as LangChain, LangGraph or similar technologies Understanding of MLOps, LLMOps and model lifecycle management principles Experience designing APIs and integrating AI solutions into enterprise environments Strong understanding of modern ...

enabled environments Experience with LLMs, prompt engineering, embeddings, semantic search and RAG patterns Exposure to vector databases and agent frameworks such as LangChain, LangGraph or similar technologies Understanding of MLOps, LLMOps and model lifecycle management principles Experience designing APIs and integrating AI solutions into enterprise environments Strong understanding of modern ...

enabled environments Experience with LLMs, prompt engineering, embeddings, semantic search and RAG patterns Exposure to vector databases and agent frameworks such as LangChain, LangGraph or similar technologies Understanding of MLOps, LLMOps and model lifecycle management principles Experience designing APIs and integrating AI solutions into enterprise environments Strong understanding of modern ...

systems and real-world applications. Key skills and experience include: Strong Python development experience Hands-on experience with LLMs and agent frameworks (e.g. LangChain, LlamaIndex, AutoGen or similar) Understanding of prompt engineering, RAG pipelines, and tool-using AI systems Experience working with APIs, data pipelines, and cloud platforms (AWS, Azure ...

enabled environments Experience with LLMs, prompt engineering, embeddings, semantic search and RAG patterns Exposure to vector databases and agent frameworks such as LangChain, LangGraph or similar technologies Understanding of MLOps, LLMOps and model lifecycle management principles Experience designing APIs and integrating AI solutions into enterprise environments Strong understanding of modern ...

enabled environments Experience with LLMs, prompt engineering, embeddings, semantic search and RAG patterns Exposure to vector databases and agent frameworks such as LangChain, LangGraph or similar technologies Understanding of MLOps, LLMOps and model lifecycle management principles Experience designing APIs and integrating AI solutions into enterprise environments Strong understanding of modern ...

enabled environments Experience with LLMs, prompt engineering, embeddings, semantic search and RAG patterns Exposure to vector databases and agent frameworks such as LangChain, LangGraph or similar technologies Understanding of MLOps, LLMOps and model lifecycle management principles Experience designing APIs and integrating AI solutions into enterprise environments Strong understanding of modern ...

Curiosity about AI‐enabled systems and how they scale responsibly Experience with LLMs, AI agent orchestration, or tools such as RAG, vector databases or LangChain is a bonus, but not essential. What’s in it for you Flexible hybrid working 25 days leave, 2 days paid for volunteering and life ...

experience developing AI/ML applications and data-driven solutions • Excellent knowledge of Python and core data science libraries such as pandas, scikit-learn, LangChain, etc. • Experience with LLMs, including foundation model APIs, prompt engineering, and retrieval augmented generation • Substantial depth and breadth across state-of-the-art AI/ ...

lifecycle best practices Hands-on experience with ML libraries: PyTorch, PySpark, scikit-learn, Hugging Face Transformers Proficiency in AI tooling: Azure ML, Databricks, MLflow, LangChain, LangGraph Proven experience with Git, unit testing, containerisation and agile methodologies (Jira, Confluence) Good knowledge of generative AI, machine learning, deep learning or NLP Strong ...