15 of 15 Part-Time LangChain Jobs in the City of London

Pandas, scikit-learn, PyTorch, TensorFlow. Proven experience building and scaling GenAI applications in production. Expertise in LLM architectures (OpenAI, LLaMA, Mistral, etc.) and RAG implementation. Familiarity with frameworks like LangChain, LlamaIndex, LangGraph, CrewAI, AutoGen, or similar. Experience with CI/CD for AI (GenAIOps), model monitoring, and cloud platforms: Google Vertex AI, AWS SageMaker, or Azure AI. Knowledge of AI More ❯

Familiarity with AI/ML Ops pipelines , real-time analytics, or edge deployments. Big Data stack knowledge (e.g., Hadoop, Spark, Kafka). GenAI/LLM experience (e.g., AWS Bedrock, LangChain). Why this is a great move Mission & impact: Work on projects where data-driven decisions have real-world consequences. Growth: Multiple openings from mid-level to Principal; training & leadership More ❯

and a strong interest in general software engineering principles. You have worked with common python frameworks (Numpy, Pandas) Demonstrated expertise with agentic AI systems , including orchestration frameworks (LangGraph preferred, LangChain or similar also considered). Strong foundation in classical machine learning and applied modeling techniques , including regression, classification, clustering, and practical experience with models used in econometrics or marketing measurement. More ❯

projects. Ideal Qualifications (Nice-to-Haves) API Integration: Practical experience integrating with third-party APIs and services within AI workflows. AI Frameworks: Familiarity with AI development frameworks such as LangChain or LangGraph. Vector Databases: Understanding of or experience with vector databases (e.g., Pinecone, Weaviate) for managing embeddings. Cloud Platforms: Familiarity with cloud services for deploying and managing applications (e.g., Google More ❯

with FastAPI or Flask Orchestration and automation, for example Airflow or asyncio Developed applications using latest LLMs (Anthropic, META, Google, Mistral etc) Experience with multi agent orchestration frameworks (LangGraph, LangChain, MCP, CrewAI etc) Experience in end2end development and deployment of ML models Experience with financial data APIs or market data feeds Nice to have Azure AI Services or Cognitive Search More ❯

solving, and leadership skills. Nice to have Experience with rerankers (e.g., cross-encoders), hybrid retrieval (SQL + vectors), query expansion, or lightweight knowledge graphs. Familiarity with LLM evaluation tooling (LangChain, LlamaIndex, OpenAI Evals) and observability for cost, relevance, and latency. Background in B2B data products or fintech. More ❯

and distillation for low-latency inference. Develop multi-step reasoning agents and autonomous workflows powered by LLMs. Build planning, decision-making, and task execution pipelines using tools such as LangChain or custom orchestrators. Implement safety, validation, and ethical guardrails for autonomous AI behaviors. Curate and preprocess large-scale text, code, and multimodal datasets for supervised, self-supervised, and agentic learning. … Build retrieval-augmented generation (RAG) pipelines and task-oriented AI agents for enterprise applications. Integrate AI agents with APIs, chatbots, and knowledge management systems. Evaluate and adopt emerging frameworks (LangChain, LlamaIndex, Hugging Face Transformers) to accelerate agentic AI deployment. Optimize distributed training on GPUs/TPUs using DeepSpeed, Ray, or Horovod. Implement mixed-precision training, model sharding, and checkpointing strategies More ❯

in AI/ML Expert in NLP and BERT based Transformer models Developed applications using latest LLMs (Anthropic, META, Google, Mistral etc) Experience with multi agent orchestration frameworks (LangGraph, LangChain, MCP, CrewAI etc) Experience in end2end development and deployment of ML models Able to be SC cleared - Must hold British passport (we have MoD projects) There are also some things More ❯

infrastructure/model deployments using Kubernetes, Terraform, and CI/CD pipelines. Building and managing vector databases and retrieval systems to support RAG pipelines. Working with LLM frameworks like LangChain , LangGraph , CrewAI , or similar tools. Partnering closely with engineering, security, and product teams to deliver safe, production-ready AI systems. What Theyre Looking For: Strong background in Software or Platform More ❯

have: 1+ year of fullstack experience (open to senior/staff-level engineers too) Production experience with Model Context Protocol (MCP) is highly desired Experience working with LLMs, RAGs, Langchain, OpenAI, Claude etc is beneficial Generative AI Product experience Production-grade Python skills Frontend experience with Vue + TypeScript , or React and a willingness to learn Vue Understanding of web More ❯

Architectural skills and experience with microservices. A genuine interest in AI and LLMs from using tools like Cursor and Copilot to experimenting with frameworks such as Hugging Face or LangChain is highly desirable. AI literacy and an interest in using AI tools to improve workflow and product outcomes. Degree in Computer Science (or similar) from a good University highly desirable. … Kafka, SQS/SNS, RabbitMQ). Knowledge of real-time streaming (Kafka Streams, Apache Flink, etc.). Exposure to big-data or machine-learning frameworks (TensorFlow, PyTorch, Hugging Face, LangChain). Understanding of infrastructure and DevOps (Terraform, Ansible, AWS, Kubernetes). Exposure to fintech or start-up environments. Mindset & Attributes: Positive, objective-focused, and collaborative. Willingness to teach and learn More ❯

where this was necessary. You either have professional experience using LLMs or have enthusiastically experimented in your own time. Industry experience with LLM toolchains and ecosystems (e.g. libraries like Langchain, observability tooling, RAG systems, etc.) is a plus. You can work closely with a Product Manager to understand customer requirements, build a vision for your team, and turn that into More ❯

Build and deploy AI/ML systems that boost accuracy, speed, and user experience. Partner with domain experts to turn complex workflows into AI-driven automation . Leverage LLMs, LangChain, and RAG at scale. Ship quickly, iterate fast, and set the foundation for our engineering culture . Requirements 2+ years in AI/ML engineering or applied research. Proven track … from idea production with measurable impact. Deep hands-on expertise with LLMs (fine-tuning, prompt engineering, evaluation, and deployment at scale). Hands-on experience with PyTorch/TensorFlow, LangChain, vector databases , or similar tools. Excellent communicator; comfortable working with non-technical stakeholders. Thrive in chaos and love building zero-to-one . This isnt just another job - its a 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 ❯

If you What Youll Do Design, implement, and ship production-grade LLM applications (prompting, RAG, evaluation, deployment). Develop and refine AI pipelines and retrieval systems using tools like LangChain, LangGraph, or custom frameworks. Own model evaluation frameworks to ensure quality, factuality, and performance across real-world workloads. Collaborate closely with the founding engineering team to build scalable infrastructure for … experience, including 23+ years in AI/ML applications. Proven track record of shipping AI products into production (not just research or prototypes). Preferred tech stack - TypeScript, React, LangChain, and LLM orchestration. Experience with RAG systems , vector databases (e.g. Pinecone, PGVector, Weaviate), and model evaluation . Comfortable working in a fast-paced startup environment with a high degree of More ❯