of Git/GitHub workflows and DevOps tooling Nice to have: Experience with Docker or multi-container application architecture Familiarity with AI/ML technologies such as LLMs, NLP, LangGraph, PydanticAI, or AutoGen Experience with biological or scientific datasets (genomics, proteomics, etc.) Exposure to frontend development (React preferred) Experience benchmarking and improving AI/ML models or agent-based systems More ❯
or persona-based evaluations. Knowledge of AI/ML approaches and deployment of AI/ML powered applications – especially using language models or NLP and agent-based approaches (e.g. LangGraph, PydanticAI, AutoGen), Knowledge of AI/ML evaluation and benchmarking approaches, experience with iterative improvement of AI/ML models and products. Research experience (e.g., Master’s project, internship at More ❯
or persona-based evaluations. Knowledge of AI/ML approaches and deployment of AI/ML powered applications – especially using language models or NLP and agent-based approaches (e.g. LangGraph, PydanticAI, AutoGen), Knowledge of AI/ML evaluation and benchmarking approaches, experience with iterative improvement of AI/ML models and products. Research experience (e.g., Master’s project, internship at More ❯
City of London, London, England, United Kingdom Hybrid / WFH Options
Ada Meher
work on production level LLM/Gen AI projects Strong experience with Python/FastAPI, Node/Typescript or similar Experience with relevant technologies such as OpenAI, LangChain/LangGraph, LlamaIndex Experience with Hugging Face and LoRA/QLoRA for fine-tuning Experience with RAG & Vector DBs eg. FAISS, Weaviate, Pinecone Any experience of MLOps with MLFlow, AWS (SageMaker), CI More ❯
multi-container applications Demonstrated experience with biological or scientific data (e.g. genomics, transcriptomics, proteomics), or pharmaceutical industry experience, especially design of data visualisations Knowledge of agent-based approaches (e.g. LangGraph, PydanticAI, AutoGen More ❯
/GitHub) Preferred qualifications: Knowledge of AI/ML approaches and deployment of AI/ML powered applications - especially using language models or NLP and agent-based approaches (e.g. LangGraph, PydanticAI, AutoGen) Prior experience conducting user research for AI applications Knowledge of AI/ML evaluation and benchmarking approaches, including safety and robustness evaluations Web-based front end development experience More ❯
containerized applications, particularly multi-container architecture.Experience with biological or scientific data (e.g., genomics, transcriptomics, proteomics) or pharmaceutical industry experience, especially in designing data visualisations.Knowledge of agent-based approaches (e.g., LangGraph, PydanticAI, AutoGen).If you're looking for a role where your skills will make a significant impact, apply now with your updated CV to become a part of our dynamic More ❯
particularly multi-container architecture. Experience with biological or scientific data (e.g., genomics, transcriptomics, proteomics) or pharmaceutical industry experience, especially in designing data visualisations. Knowledge of agent-based approaches (e.g., LangGraph, PydanticAI, AutoGen). If you're looking for a role where your skills will make a significant impact, apply now with your updated CV to become a part of our More ❯
distillation of large-scale language models, with hands-on expertise in SFT, PPO, and reward modeling. Deep proficiency in Python and AI/ML frameworks such as PyTorch, LangChain, LangGraph, GraphRAG, and AutoGen. Experience with modern vector and graph databases (e.g., ChromaDB, Neo4j) and LLMOps platforms (e.g., Azure, Databricks, Azure OpenAI). Proven track record of delivering scalable AI solutions More ❯
GitHub). Preferred Qualifications Knowledge of AI/ML approaches and deployment of AI/ML powered applications – especially using language models or NLP and agent-based approaches (e.g. LangGraph, PydanticAI, AutoGen). Prior experience conducting user research for AI applications. Knowledge of AI/ML evaluation and benchmarking approaches, including safety and robustness evaluations. Web-based front end development More ❯
GitHub). Preferred Qualifications Knowledge of AI/ML approaches and deployment of AI/ML powered applications – especially using language models or NLP and agent-based approaches (e.g. LangGraph, PydanticAI, AutoGen). Prior experience conducting user research for AI applications. Knowledge of AI/ML evaluation and benchmarking approaches, including safety and robustness evaluations. Web-based front end development More ❯
GitHub). Preferred Qualifications Knowledge of AI/ML approaches and deployment of AI/ML powered applications – especially using language models or NLP and agent-based approaches (e.g. LangGraph, PydanticAI, AutoGen). Prior experience conducting user research for AI applications. Knowledge of AI/ML evaluation and benchmarking approaches, including safety and robustness evaluations. Web-based front end development More ❯
SOTA in multi-agent systems, collective learning, AI safety, secure agent execution, emerging AI agent architectures, and tool integration. Familiarity with one or more multi-agent frameworks (such as LangGraph, LangChain, CrewAI, AutoGen, and Pydantic AI), communication standards (such as MCP, A2A, Story's Agent TCP/IP, Near's AITP), distributed systems, distributed AI architectures, and consensus mechanisms. Ability More ❯
drive significant innovation and efficiency within our global financial services operations. Core Responsibilities Architect Autonomous Agents: Design and implement robust, goal-driven AI agents using leading frameworks like LangChain, LangGraph, and the Google Agent Development Kit (ADK). Develop and Evaluate RAG Pipelines: Engineer and optimize end-to-end Retrieval-Augmented Generation (RAG) systems, including data ingestion, chunking strategies, and … Python and FastAPI to integrate agentic systems into the wider enterprise architecture. Technical Skillset Requirements Core AI & Frameworks: Agentic Frameworks: Expert-level knowledge of agentic frameworks such as LangChain, LangGraph, Google Agent Development Kit (ADK) LLM Expertise: Advanced Prompt Engineering and hands-on experience with model fine-tuning techniques including PEFT and QLoRA. Proven experience with models like Gemini, and More ❯
drive significant innovation and efficiency within our global financial services operations. Core Responsibilities Architect Autonomous Agents: Design and implement robust, goal-driven AI agents using leading frameworks like LangChain, LangGraph, and the Google Agent Development Kit (ADK). Develop and Evaluate RAG Pipelines: Engineer and optimize end-to-end Retrieval-Augmented Generation (RAG) systems, including data ingestion, chunking strategies, and … Python and FastAPI to integrate agentic systems into the wider enterprise architecture. Technical Skillset Requirements Core AI & Frameworks: Agentic Frameworks: Expert-level knowledge of agentic frameworks such as LangChain, LangGraph, Google Agent Development Kit (ADK) LLM Expertise: Advanced Prompt Engineering and hands-on experience with model fine-tuning techniques including PEFT and QLoRA. Proven experience with models like Gemini, and More ❯
City of London, London, United Kingdom Hybrid / WFH Options
Oliver Bernard
experience, ideally working in high-growth start-up or scale-up environments Strong skills with Python & Fast API Cloud experience, ideally with AWS Proficiency with LLMs, Pydantic, LangChain/LangGraph or similar orchestration tools Computer Science/STEM background from a top ranked University Base salary of £80k-£110k Exceptional equity and benefits package Hybrid working, with 2-3 days More ❯
modern software development tools and practices (e.g., Git/GitHub). Experience deploying AI/ML-powered applications, especially those involving NLP, language models, or agent-based frameworks (e.g., LangGraph, PydanticAI, AutoGen). Background in user research specific to AI applications. Knowledge of AI/ML evaluation techniques, including safety and robustness assessments. Experience in web-based frontend development. If More ❯
agile development cycles and code reviews Key Skills: Python, React, TypeScript, Git/GitHub, cloud infrastructure (GCP), agile development, automated testing Nice to Have: Experience with AI safety evaluations, LangGraph, PydanticAI, or AutoGen frameworks Ideal for: Full stack engineers interested in building user-facing AI tools and safety-focused GenAI systems. More ❯
scaling beyond one million users. Play a foundational role in growing the engineering team over time, including mentoring and hiring. Nice to Have Experience with orchestration frameworks (e.g., LangChain, LangGraph) and multi-agent workflows. Familiarity with vector databases, RAG pipelines, and lightweight model hosting. Ability to set up data pipelines and feedback loops for improving AI-driven features. Awareness of More ❯
London, UK Hybrid: 3 days a week from office JD: AI Lead to drive the development and deployment of next-generation agentic AI solutions using Azure OpenAI GPT-5, LangGraph frameworks, and intelligent document processing. Lead technical workstreams in building production-ready AI systems for financial automation with hands-on development approach. . click apply for full job details More ❯
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 RAG Strong More ❯
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 RAG Strong More ❯
Datasmoothie is a data analysis solution designed specifically for market researchers.They are part of the Global Data Management Tower within Ipsos and their products offer an all-in-one solutions that helps users create and manage their data pipeline automating More ❯
South West London, London, United Kingdom Hybrid / WFH Options
Purview Consultancy Services Ltd
operations. Required Qualifications 15+ years of experience in AI/ML architecture with 8+ years in enterprise AI solutions Deep expertise in LLM architectures, prompt engineering, and agentic frameworks (LangGraph, LangMem) Hands-on experience with Azure OpenAI GPT-4/5, embedding models, and Azure cloud services Strong background in Python, distributed systems, and enterprise architecture Experience with Claude Code … workloads (1M+ documents, sub-second response times) Key Responsibilities Design end-to-end AI solutions for private equity fund operations and financial automation Architect scalable agentic AI frameworks using LangGraph, LangMem, and custom agent orchestration Lead technical strategy for Azure OpenAI GPT-5 integration and advanced embedding-based retrieval systems Design and implement advanced RAG architectures including hybrid search, query … failover and load balancing Establish comprehensive monitoring, observability, and performance optimization strategies Mentor technical teams and establish AI engineering best practices using modern toolchains Oversee model performance evaluation using LangGraph evals and DeepEval frameworks More ❯
