18 of 18 Contract LangChain Jobs

applications, optimizing performance and scalability. Apply backend/data processing skills to handle structured and unstructured datasets. Stay current with open-source AI/ML frameworks (e.g., Hugging Face, LangChain) and apply them to business needs. Communicate technical concepts clearly to technical and non-technical stakeholders. Essential Skills & Experience Hands-on experience in ML, NLP, and GenAI (LLM/SLM More ❯

desirable). Proven experience deploying and supporting LLMs in production. Strong understanding of LLM fine-tuning (PyTorch, TensorFlow, Hugging Face Trainer, etc.). Experience with ML tooling (e.g. SageMaker, LangChain/LangSmith, MLflow, Dataiku, DataRobot). Knowledge of embeddings, their applications, and limitations. Hands-on experience in Agile/Lean/XP environments. Excellent communication, problem-solving, and cross-team More ❯

of NLP, transformer models, and generative AI principles. Hands-on experience with platforms such as OpenAI, Anthropic, Cohere, or Hugging Face. Proficiency in Python and familiarity with tools like LangChain, PromptLayer, or similar. Excellent analytical, problem-solving, and communication skills. Preferred Qualifications: Experience with prompt tuning, fine-tuning, or reinforcement learning from human feedback (RLHF). Familiarity with multi-modal More ❯

Hands-on experience with LLMs/SLMs, Python, Java, SQL/NoSQL. API development, web scraping, data integration. On-prem AI model deployment, open-source frameworks (e.g. Hugging Face, LangChain). Strong consulting and communication skills. Preferred Knowledge of data structures, algorithms, and solution architecture. Experience with cloud AI platforms (AWS, Azure, GCP). Broad ML knowledge and prior consulting More ❯

Studio, Power Automate, Power Apps, and custom connectors. Proficiency in Microsoft Graph API, Azure OpenAI, and Semantic Kernel. Solid programming skills in Python and/or C#. Familiarity with LangChain, AutoGen, or similar orchestration frameworks. Experience with Azure AI Search, Form Recognizer, and Language Services. Knowledge of CI/CD, DevSecOps practises, and responsible AI frameworks. We are searching for More ❯

Studio, Power Automate, Power Apps, and custom connectors. Proficiency in Microsoft Graph API, Azure OpenAI, and Semantic Kernel. Solid programming skills in Python and/or C#. Familiarity with LangChain, AutoGen, or similar orchestration frameworks. Experience with Azure AI Search, Form Recognizer, and Language Services. Knowledge of CI/CD, DevSecOps practises, and responsible AI frameworks. We are searching for More ❯

Experience with GitOps tools such as FluxCD, and integrating AI into GitOps workflows Knowledge of handling compliance-sensitive data in AI contexts Experience with prompt engineering or frameworks like LangChain, Semantic Kernel Experience with or contributions to a developer portal such as Backstage More ❯

sectors like travel, e-commerce, or hospitality Conversational interfaces, semantic search, or recommendation engines Ethical AI frameworks, GDPR, and trust-by-design principles Building AI features using tools like LangChain, Pinecone, or RAG architectures More ❯

sectors like travel, e-commerce, or hospitality Conversational interfaces, semantic search, or recommendation engines Ethical AI frameworks, GDPR, and trust-by-design principles Building AI features using tools like LangChain, Pinecone, or RAG architectures More ❯

No-Code: Ability to leverage low-code and no-code solutions where appropriate to accelerate delivery. Nice to Have Exposure to agentic AI, including experience with LLMs, MCP servers, LangChain, and prompt engineering. To apply, please submit your CV and daily rate expectation by clicking on the relevant links. If successful you will be contacted by one of our consultants More ❯

No-Code: Ability to leverage low-code and no-code solutions where appropriate to accelerate delivery. Nice to Have Exposure to agentic AI, including experience with LLMs, MCP servers, LangChain, and prompt engineering. To apply, please submit your CV and daily rate expectation by clicking on the relevant links. If successful you will be contacted by one of our consultants More ❯

HR function. You will work closely with HR stakeholders, data scientists, and legal compliance teams to build secure and scalable multi-agent AI solutions using modern frameworks such as LangChain, AutoGen, LangGraph, and RAG. Key Responsibilities: Architect and implement AI-powered tools to automate and enhance HR workflows (e.g., onboarding, employee queries, policy navigation). Design and deploy conversational agents … using LangChain and AutoGen for internal use. Build modular and scalable pipelines using LangGraph for multi-agent orchestration. Integrate RAG systems to enable context-aware document retrieval from internal HR/legal databases. Required Skills & Experience: Solid exposure in AI/ML engineering, with a strong focus on NLP and LLMs. Proven hands-on knowledge with: LangChain, AutoGen, LangGraph and More ❯

practices, and observability tools to ensure reliable, high-performing systems. On the AI side, you have practical experience working with large language models (LLMs), prompt engineering, and frameworks like LangChain, with knowledge of advanced patterns such as Retrieval-Augmented Generation (RAG) and agent-based systems. A strong foundation in Agile SDLC practices is essential, along with familiarity in containerization, DevOps … in building microservices, SaaS solutions, and API-first architectures to enable scalable, interoperable systems. Frameworks & Tools: Backend: Spring Boot (web, data, cloud, security), JUnit 5, Mockito, database integration. AI: LangChain, Retrieval-Augmented Generation (RAG), MCP servers (as consumer and developer), and prompt engineering for LLM optimization. Exposure to popular libraries and frameworks (Apache Commons, Guava, Swagger, TestContainers). Architecture & Platforms … application security and model safety considerations. AI Development: End-to-end development of AI-powered solutions, from concept to production readiness. Practical experience with LLMs and agentic AI, including LangChain integration, RAG pipelines, prompt engineering, and model consumption via APIs. Ability to bridge backend and AI workflows, integrating advanced AI capabilities into scalable enterprise platforms. SDLC & Agile Practices: Familiar with More ❯

practices, and observability tools to ensure reliable, high-performing systems. On the AI side, you have practical experience working with large language models (LLMs), prompt engineering, and frameworks like LangChain, with knowledge of advanced patterns such as Retrieval-Augmented Generation (RAG) and agent-based systems. A strong foundation in Agile SDLC practices is essential, along with familiarity in containerization, DevOps … in building microservices, SaaS solutions, and API-first architectures to enable scalable, interoperable systems. Frameworks & Tools:Backend: Spring Boot (web, data, cloud, security), JUnit 5, Mockito, database integration. AI: LangChain, Retrieval-Augmented Generation (RAG), MCP servers (as consumer and developer), and prompt engineering for LLM optimization. Exposure to popular libraries and frameworks (Apache Commons, Guava, Swagger, TestContainers). Architecture & Platforms … application security and model safety considerations. AI Development: End-to-end development of AI-powered solutions, from concept to production readiness. Practical experience with LLMs and agentic AI, including LangChain integration, RAG pipelines, prompt engineering, and model consumption via APIs. Ability to bridge backend and AI workflows, integrating advanced AI capabilities into scalable enterprise platforms. SDLC & Agile Practices: Familiar with More ❯

practices, and observability tools to ensure reliable, high-performing systems. On the AI side, you have practical experience working with large language models (LLMs), prompt engineering, and frameworks like LangChain, with knowledge of advanced patterns such as Retrieval-Augmented Generation (RAG) and agent-based systems. A strong foundation in Agile SDLC practices is essential, along with familiarity in containerization, DevOps … in building microservices, SaaS solutions, and API-first architectures to enable scalable, interoperable systems. Frameworks & Tools: Backend: Spring Boot (web, data, cloud, security), JUnit 5, Mockito, database integration. AI: LangChain, Retrieval-Augmented Generation (RAG), MCP servers (as consumer and developer), and prompt engineering for LLM optimization. Exposure to popular libraries and frameworks (Apache Commons, Guava, Swagger, TestContainers). Architecture & Platforms … application security and model safety considerations. AI Development: End-to-end development of AI-powered solutions, from concept to production readiness. Practical experience with LLMs and agentic AI, including LangChain integration, RAG pipelines, prompt engineering, and model consumption via APIs. Ability to bridge backend and AI workflows, integrating advanced AI capabilities into scalable enterprise platforms. SDLC & Agile Practices: Familiar with More ❯

of-the-art tools and platforms. Essential Skills for the Role: Python (FastAPI): Proficiency in Python, particularly with the FastAPI framework, is vital to develop robust, scalable AI solutions. LangChain: Practical experience using LangChain to integrate and operationalize large language models. Google AI SDK & Azure Open AI SDK: Expertise with these SDKs to deploy and manage AI capabilities within GCP More ❯

of-the-art tools and platforms. Essential Skills for the Role: Python (FastAPI): Proficiency in Python, particularly with the FastAPI framework, is vital to develop robust, scalable AI solutions. LangChain: Practical experience using LangChain to integrate and operationalize large language models. Google AI SDK & Azure Open AI SDK: Expertise with these SDKs to deploy and manage AI capabilities within GCP More ❯

vector stores (Chroma, FAISS, Milvus) with sensitive document ingestion pipelines - Host and optimise LLMs using vLLM/TGI on local GPU clusters without internet connectivity - Implement agent orchestration using LangChain/LangGraph in completely offline environments - Design secure document processing for classified materials with appropriate data sanitisation - Build monitoring and evaluation systems that operate within air-gapped infrastructure Essential Requirements … optimisation - Python expertise with offline dependency management and local package mirrors Technical Stack (All On-Premises) Models: Llama 3, Mistral, Qwen (locally hosted) Vector Stores: Chroma, FAISS, Milvus Orchestration: LangChain, LangGraph for agents Hosting: vLLM, TGI, Ollama on bare metal/private cloud Infrastructure: Air-gapped Kubernetes, local container registries Desirable Skills - Experience with defence/government IT security protocols More ❯