/unsupervised Model tuning, MLOps/LLMOps pipelines, and AI observability. Experience in Enterprise-grade RAG-based solutions with LLMs (OpenAI, Hugging Face, LLaMA, etc.) and vector databases (Pinecone, Weaviate, FAISS, etc.). Ability to design enterprise-level AI/Gen AI platform/solutions with the client’s existing enterprise stack. Hands-on mastery of core GenAI frameworks (e.g. More ❯
processes. Master Embedding Strategies: Create and manage high-quality vector embeddings for semantic search, text classification, and other NLP tasks. You will work extensively with vector databases like Pinecone, Weaviate, or Chroma. Construct LLM Chains and Graphs: Utilize LangChain or LangGraph to develop, prototype, and productionize complex, stateful applications and workflows powered by LLMs. Model Integration & Deployment: Fine-tune, evaluate More ❯
for building scalable, maintainable, and production-ready AI systems. Experience in designing and implementing enterprise-grade AI solutions, including RAG-based solutions with LLMs and vector databases (e.g., Pinecone, Weaviate, FAISS). Proven experience in full stack development and AI/ML system implementation within enterprise environments. Strong grasp of advanced techniques such as complex task decomposition for agents, reasoning More ❯
for building scalable, maintainable, and production-ready AI systems. Experience in designing and implementing enterprise-grade AI solutions, including RAG-based solutions with LLMs and vector databases (e.g., Pinecone, Weaviate, FAISS). Proven experience in full stack development and AI/ML system implementation within enterprise environments. Strong grasp of advanced techniques such as complex task decomposition for agents, reasoning More ❯
London, South East, England, United Kingdom Hybrid / WFH Options
IT Graduate Recruitment
Augmented Generation, Python, Data Science, AI Research, MLOps, Data Pipelines, Prompt Engineering, Model Fine-Tuning, Cloud Computing, AWS, Azure, Google Cloud, AI Infrastructure, Transformers, Reinforcement Learning, Vector Databases, Pinecone, Weaviate, Semantic Search, API Development, AI Deployment, Model Serving, AI Automation, Early Stage Startup, AI Startups, Tech Startup, Machine Intelligence, Applied AI, AI Applications, AI Innovation, AI Product Development, AI Tools More ❯
Llama, Mistral) and GenAI/Agentic frameworks (LangChain, LlamaIndex, AutoGen, CrewAI) Proven track record designing scalable AI architectures (RAG, Graph RAG, multi-agent systems) Experience with vector databases (Pinecone, Weaviate, FAISS) and major cloud platforms (AWS, Azure, GCP) Strong foundation in MLOps/LLMOps and Agile delivery Excellent leadership, communication, and stakeholder engagement skills 10+ years' experience in AI/ More ❯
data-driven applications Strong programming skills in Python (e.g. PyTorch, TensorFlow, scikit-learn) and/or TypeScript/JavaScript Familiarity with LLM frameworks (LangChain, LlamaIndex) and vector databases (Pinecone, Weaviate) Understanding of cloud platforms (AWS, Azure, or GCP) and modern development workflows (Git, CI/CD) Excellent problem-solving skills and a proactive, collaborative mindset Full right to work in More ❯
data-driven applications Strong programming skills in Python (e.g. PyTorch, TensorFlow, scikit-learn) and/or TypeScript/JavaScript Familiarity with LLM frameworks (LangChain, LlamaIndex) and vector databases (Pinecone, Weaviate) Understanding of cloud platforms (AWS, Azure, or GCP) and modern development workflows (Git, CI/CD) Excellent problem-solving skills and a proactive, collaborative mindset Full right to work in More ❯
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 Cloud Run, AWS Lambda) is a plus. Our generous benefits package includes: Hybrid working More ❯
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 Cloud Run, AWS Lambda) is a plus. Our generous benefits package includes: Hybrid working More ❯
SR2 | Socially Responsible Recruitment | Certified B Corporation™
SaaS or data-driven business. Strong knowledge of LLMs , prompt engineering, and fine-tuning approaches. Hands-on experience with AI/ML pipelines and vector databases (e.g. Pinecone, FAISS, Weaviate). Proficiency in Python plus at least one other backend language (TypeScript or Java preferred). Proven experience with AWS , containerisation, and infrastructure as code (Terraform, Docker). Solid understanding More ❯
City of London, London, United Kingdom Hybrid / WFH Options
SR2 | Socially Responsible Recruitment | Certified B Corporation™
SaaS or data-driven business. Strong knowledge of LLMs , prompt engineering, and fine-tuning approaches. Hands-on experience with AI/ML pipelines and vector databases (e.g. Pinecone, FAISS, Weaviate). Proficiency in Python plus at least one other backend language (TypeScript or Java preferred). Proven experience with AWS , containerisation, and infrastructure as code (Terraform, Docker). Solid understanding More ❯
and governance standards. Prepare and curate training datasets (structured/unstructured text, images, code). Apply data preprocessing, tokenization, and embedding generation techniques. Work with vector databases (e.g., Pinecone, Weaviate, FAISS, Chroma) for semantic search and retrieval. Partner with business stakeholders to identify and shape impactful AI use cases. Contribute to the development of a strategic AI adoption roadmap and More ❯
and governance standards. Prepare and curate training datasets (structured/unstructured text, images, code). Apply data preprocessing, tokenization, and embedding generation techniques. Work with vector databases (e.g., Pinecone, Weaviate, FAISS, Chroma) for semantic search and retrieval. Partner with business stakeholders to identify and shape impactful AI use cases. Contribute to the development of a strategic AI adoption roadmap and More ❯
and governance standards. Prepare and curate training datasets (structured/unstructured text, images, code). Apply data preprocessing, tokenization, and embedding generation techniques. Work with vector databases (e.g., Pinecone, Weaviate, FAISS, Chroma) for semantic search and retrieval. Partner with business stakeholders to identify and shape impactful AI use cases. Contribute to the development of a strategic AI adoption roadmap and More ❯
and governance standards. Prepare and curate training datasets (structured/unstructured text, images, code). Apply data preprocessing, tokenization, and embedding generation techniques. Work with vector databases (e.g., Pinecone, Weaviate, FAISS, Chroma) for semantic search and retrieval. Partner with business stakeholders to identify and shape impactful AI use cases. Contribute to the development of a strategic AI adoption roadmap and More ❯
london (city of london), south east england, united kingdom
Luxoft
and governance standards. Prepare and curate training datasets (structured/unstructured text, images, code). Apply data preprocessing, tokenization, and embedding generation techniques. Work with vector databases (e.g., Pinecone, Weaviate, FAISS, Chroma) for semantic search and retrieval. Partner with business stakeholders to identify and shape impactful AI use cases. Contribute to the development of a strategic AI adoption roadmap and More ❯
City of London, London, United Kingdom Hybrid / WFH Options
SR2 | Socially Responsible Recruitment | Certified B Corporation™
SaaS or data-driven business. Strong knowledge of LLMs , prompt engineering, and fine-tuning approaches. Hands-on experience with AI/ML pipelines and vector databases (e.g. Pinecone, FAISS, Weaviate). Proficiency in Python plus at least one other backend language (TypeScript or Java preferred). Proven experience with AWS , containerisation, and infrastructure as code (Terraform, Docker). Solid understanding More ❯
SR2 | Socially Responsible Recruitment | Certified B Corporation™
SaaS or data-driven business. Strong knowledge of LLMs , prompt engineering, and fine-tuning approaches. Hands-on experience with AI/ML pipelines and vector databases (e.g. Pinecone, FAISS, Weaviate). Proficiency in Python plus at least one other backend language (TypeScript or Java preferred). Proven experience with AWS , containerisation, and infrastructure as code (Terraform, Docker). Solid understanding More ❯
Strong understanding of machine learning frameworks (TensorFlow, PyTorch, Scikit-learn) Experience with LLM integration (OpenAI, Anthropic, open-source models) Knowledge of RAG architectures, prompt engineering, and vector databases (Pinecone, Weaviate) Experience with MLOps tools and monitoring model performance in production Automation Architecture Deep knowledge of automation tools including GitHub Actions, Terraform, and Ansible Experience with business process automation (RPA) tools More ❯
sources. Design RAG systems : chunking strategies, document schemas, metadata, hybrid/dense retrieval, re-ranking, and grounding. Manage vector/keyword indexes (e.g., Azure AI Search , pgvector, Pinecone/Weaviate). Develop and deploy advanced NLP, information retrieval, and recommendation systems that enhance Chambers and Partners’ research and product offerings, including document understanding, automatic summarisation, topic modelling, semantic search, entity More ❯
a client-facing, consultative role. Deep knowledge of microservices, distributed systems, and event-driven architectures. Strong foundation in AI/ML, especially LLMs, RAG , and vector databases (e.g. Pinecone, Weaviate). Experience with enterprise data integration (e.g. PostgreSQL, SharePoint, APIs, warehouses). Cloud architecture experience (AWS, Azure, or GCP). Proficiency in Python (strongly preferred), and familiarity with Docker and More ❯
a client-facing, consultative role. Deep knowledge of microservices, distributed systems, and event-driven architectures. Strong foundation in AI/ML, especially LLMs, RAG , and vector databases (e.g. Pinecone, Weaviate). Experience with enterprise data integration (e.g. PostgreSQL, SharePoint, APIs, warehouses). Cloud architecture experience (AWS, Azure, or GCP). Proficiency in Python (strongly preferred), and familiarity with Docker and More ❯
a client-facing, consultative role. Deep knowledge of microservices, distributed systems, and event-driven architectures. Strong foundation in AI/ML, especially LLMs, RAG , and vector databases (e.g. Pinecone, Weaviate). Experience with enterprise data integration (e.g. PostgreSQL, SharePoint, APIs, warehouses). Cloud architecture experience (AWS, Azure, or GCP). Proficiency in Python (strongly preferred), and familiarity with Docker and More ❯
