Technical Leadership Define and lead the AI & Data Science vision and roadmap , aligned with business priorities. Provide technical oversight for AI initiatives across domains: Generative AI & LLMs (fine-tuning, RAG pipelines, multi-agent systems). Predictive Analytics & Time-Series Modeling . Computer Vision & Multimodal AI . Reinforcement Learning & Optimization . Knowledge Engineering & Semantic Search . Edge AI & Real-Time AI … and client engagements . Required Qualifications Significant experience in AI/ML, including experience in a technical leadership or team lead role . Strong knowledge (architectural & practical) of: LLMs, RAG, and AI Agents . Predictive analytics & time-series forecasting . Computer vision, multimodal learning, and geospatial AI . Reinforcement learning and optimization techniques . MLOps practices & data pipelines . Ability More ❯
Python preferred; additional exposure to JavaScript/TypeScript or Go beneficial). Strong background in AI systems, cloud-native architectures, and modern ML tooling (LangChain, OpenAI API, vector stores, RAG pipelines). Experience designing distributed, high-availability platforms deployed on AWS or similar cloud environments. Excellent communication and stakeholder management skills, with the ability to translate technical direction into business … to the property, SaaS, or workflow automation space is advantageous. Summary A rare opportunity for a technically strong, execution-focused engineering leader to shape and scale a next-generation AI automation platform from the ground up — with full ownership of the engineering strategy, team, and architecture. More ❯
ll Do Architecture & Development : Design and implement the cognitive systems that give AI agents consistent personalities, memory, and reasoning capabilities, using advanced LLM techniques like chain-of-thought prompting, RAG systems, and agentic tool use. Modeling & Experimentation : Design and run systematic experiments to evaluate agent behavior, test hypotheses about behavioral patterns, and iterate on model architectures based on empirical results … principles Experience working in fast-paced environments where requirements evolve rapidly Technical Skills LLM & Agent Development : Hands-on experience building applications with large language models, implementing advanced prompting techniques, RAG systems, and agentic workflows Backend Engineering : Proficient in Python and backend frameworks (e.g. FastAPI, Django, Flask); understanding of distributed systems and scalable architectures AI/ML Frameworks : Experience with PyTorch More ❯
ll Do Architecture & Development : Design and implement the cognitive systems that give AI agents consistent personalities, memory, and reasoning capabilities, using advanced LLM techniques like chain-of-thought prompting, RAG systems, and agentic tool use. Modeling & Experimentation : Design and run systematic experiments to evaluate agent behavior, test hypotheses about behavioral patterns, and iterate on model architectures based on empirical results … principles Experience working in fast-paced environments where requirements evolve rapidly Technical Skills LLM & Agent Development : Hands-on experience building applications with large language models, implementing advanced prompting techniques, RAG systems, and agentic workflows Backend Engineering : Proficient in Python and backend frameworks (e.g. FastAPI, Django, Flask); understanding of distributed systems and scalable architectures AI/ML Frameworks : Experience with PyTorch More ❯
Are you an AI engineer with strong software engineering fundamentals? Have you deployed AI models into production—not just trained them? Want to work on agentic systems, RAG pipelines, and LLM-powered tools? We're working with a high-growth GenAI consultancy building real-world, production-grade AI applications. With a team of 50+, the company partners with global clients … deployment, collaborating with cross-functional teams and mentoring other engineers. Key Responsibilities Build and deploy scalable AI tools using Python and LLM APIs Design end-to-end agentic and RAG-based solutions for enterprise use Own delivery from proof-of-concept through to production Work with cloud-native architectures (e.g. AWS Lambda, Step Functions) Lead and guide junior engineers within More ❯
Are you an AI engineer with strong software engineering fundamentals? Have you deployed AI models into production—not just trained them? Want to work on agentic systems, RAG pipelines, and LLM-powered tools? We're working with a high-growth GenAI consultancy building real-world, production-grade AI applications. With a team of 50+, the company partners with global clients … deployment, collaborating with cross-functional teams and mentoring other engineers. Key Responsibilities Build and deploy scalable AI tools using Python and LLM APIs Design end-to-end agentic and RAG-based solutions for enterprise use Own delivery from proof-of-concept through to production Work with cloud-native architectures (e.g. AWS Lambda, Step Functions) Lead and guide junior engineers within More ❯
extraction, sentiment analysis, and qualitative insights Define reporting standards: Set quality bars for what makes a great dashboard (agent performance, app store trends, CSAT drivers, topic evolution, etc.) Optimize RAG pipelines: Design retrieval strategies and grounding approaches for report generation to ensure factual, relevant outputs Customer-Facing Analytics & Enablement (30%) Deliver bespoke insights: Partner with key … for accuracy and relevance, ensure quality before launch Operations & Optimization (10%) Monitor report health: Track delivery, engagement, and quality metrics; debug when outputs degrade Performance tuning: Optimize report generation costs and latency; balance API usage with quality Document everything: Maintain clear documentation for prompts, templates, and best practices What Makes You a Great FitTechnical Foundation 5+ years in … building customer insights, product analytics, or data-driven reporting AI-native experience: Hands-on work building reports or products using LLMs—prompt engineering, structured output generation, embeddings, RAG, summarization pipelines Python proficiency: Comfortable with pandas, OpenAI library, API integrations, and data manipulation for prototyping and analysis SQL fluency: Can write complex queries and understand data modeling Analytics tools More ❯
extraction, sentiment analysis, and qualitative insights Define reporting standards: Set quality bars for what makes a great dashboard (agent performance, app store trends, CSAT drivers, topic evolution, etc.) Optimize RAG pipelines: Design retrieval strategies and grounding approaches for report generation to ensure factual, relevant outputs Customer-Facing Analytics & Enablement (30%) Deliver bespoke insights: Partner with key … for accuracy and relevance, ensure quality before launch Operations & Optimization (10%) Monitor report health: Track delivery, engagement, and quality metrics; debug when outputs degrade Performance tuning: Optimize report generation costs and latency; balance API usage with quality Document everything: Maintain clear documentation for prompts, templates, and best practices What Makes You a Great FitTechnical Foundation 5+ years in … building customer insights, product analytics, or data-driven reporting AI-native experience: Hands-on work building reports or products using LLMs—prompt engineering, structured output generation, embeddings, RAG, summarization pipelines Python proficiency: Comfortable with pandas, OpenAI library, API integrations, and data manipulation for prototyping and analysis SQL fluency: Can write complex queries and understand data modeling Analytics tools More ❯
operate as "Forward Deployed Engineers," bridging the gap between our clients' most ambitious goals and our core engineering teams. Our mission is to architect and deliver the next generation of commerce. We work on innovative projects from strategy through implementation, using the latest technologies – with a particular focus on Generative AI – to help our clients achieve market leadership. … client-facing engineering roles. Full Stack development expertise (Python, JavaScript). Hands-on cloud experience (Azure or GCP). Integration with Generative AI services and AI/ML patterns (RAG, MLOps). Strong database management skills. Nice-to-have: Cloud certifications (Azure/GCP). Experience with vector databases (Pinecone, Weaviate). Retail or CPG industry exposure. Why Accenture? Competitive … technology and operations, with digital capabilities across all of these services. With our thought leadership and culture of innovation, we apply industry expertise, diverse skill sets, and next-generation technology to each business challenge. We believe in inclusion and diversity and supporting the whole person. Our core values include Stewardship, Best People, Client Value Creation, One Global Network More ❯
operate as "Forward Deployed Engineers," bridging the gap between our clients' most ambitious goals and our core engineering teams. Our mission is to architect and deliver the next generation of commerce. We work on innovative projects from strategy through implementation, using the latest technologies – with a particular focus on Generative AI – to help our clients achieve market leadership. … client-facing engineering roles. Full Stack development expertise (Python, JavaScript). Hands-on cloud experience (Azure or GCP). Integration with Generative AI services and AI/ML patterns (RAG, MLOps). Strong database management skills. Nice-to-have: Cloud certifications (Azure/GCP). Experience with vector databases (Pinecone, Weaviate). Retail or CPG industry exposure. Why Accenture? Competitive … technology and operations, with digital capabilities across all of these services. With our thought leadership and culture of innovation, we apply industry expertise, diverse skill sets, and next-generation technology to each business challenge. We believe in inclusion and diversity and supporting the whole person. Our core values include Stewardship, Best People, Client Value Creation, One Global Network More ❯
Software Engineer who can stand on their own two feet and deliver complex solutions with confidence. You’ll be joining a small, sharp team building AI-driven products — think RAG pipelines , GenAI integrations , and LLM-powered features that actually make an impact. This isn’t a role where you hide behind Jira tickets. You’ll be hands-on , trusted to … make big decisions, and involved across the full stack. You’ll be working with: 🧠 Python (Django) ⚡ JavaScript (React or Vue) ☁️ AWS 🔁 CI/CD 💾 SQL 🤖 GenAI, RAG pipelines & API integrations What we’re looking for: Someone who can deliver end-to-end technical solutions independently Excellent communicator (small team, no silos) Real-world experience with AI/GenAI projects A More ❯
Software Engineer who can stand on their own two feet and deliver complex solutions with confidence. You’ll be joining a small, sharp team building AI-driven products — think RAG pipelines , GenAI integrations , and LLM-powered features that actually make an impact. This isn’t a role where you hide behind Jira tickets. You’ll be hands-on , trusted to … make big decisions, and involved across the full stack. You’ll be working with: 🧠 Python (Django) ⚡ JavaScript (React or Vue) ☁️ AWS 🔁 CI/CD 💾 SQL 🤖 GenAI, RAG pipelines & API integrations What we’re looking for: Someone who can deliver end-to-end technical solutions independently Excellent communicator (small team, no silos) Real-world experience with AI/GenAI projects A More ❯
how data science drives commercial outcomes. Establish an AI Centre of Excellence to embed data-driven thinking across all functions. Deliver enterprise-scale GenAI solutions (eg copilots, virtual assistants, RAG systems) and lead classical ML initiatives. Define governance and ethical standards for responsible AI adoption. What We're Looking For Extensive senior leadership experience leading large, enterprise data science functions. … A strong track record of delivering production-grade AI/ML solutions and driving adoption at scale. Deep expertise in GenAI (LLMs, prompt engineering, RAG) as well as classical ML and experimentation. Hands-on knowledge of MLOps, cloud (AWS/Azure), and modern data science tools. Exceptional stakeholder skills - able to translate complex technical work into board-level strategy and More ❯
doing: • Owning the design and build of LLM-based systems end-to-end • Fine-tuning and adapting models (LoRA, instruction tuning, PEFT etc) rather than just prompt-engineering • Building RAG workflows, embedding strategies, memory layers and domain grounding • Working out how to measure output quality, reduce hallucination risk and improve robustness • Optimising inference performance (quantisation, distillation, pruning, batching, caching) • Deploying … prototyping/notebooks • Strong Python and experience with PyTorch/Hugging Face/similar tooling • Experience deploying models into production (not just training them) • Familiarity with vector stores and RAG patterns • Comfortable operating in a hybrid environment and speaking with stakeholders where required • Someone who enjoys solving real problems end-to-end: from understanding the domain → designing the approach → shipping More ❯
doing: • Owning the design and build of LLM-based systems end-to-end • Fine-tuning and adapting models (LoRA, instruction tuning, PEFT etc) rather than just prompt-engineering • Building RAG workflows, embedding strategies, memory layers and domain grounding • Working out how to measure output quality, reduce hallucination risk and improve robustness • Optimising inference performance (quantisation, distillation, pruning, batching, caching) • Deploying … prototyping/notebooks • Strong Python and experience with PyTorch/Hugging Face/similar tooling • Experience deploying models into production (not just training them) • Familiarity with vector stores and RAG patterns • Comfortable operating in a hybrid environment and speaking with stakeholders where required • Someone who enjoys solving real problems end-to-end: from understanding the domain → designing the approach → shipping More ❯
Manchester Area, United Kingdom Hybrid/Remote Options
ConnexAI
environment. What You’ll Do Train and fine-tune LLMs and ML models using standardised experiments and high-quality data. Build and improve LLM-based systems for low-latency RAG systems, intent detection, call scoring, and email classification. Support real-time inference infrastructure with ultra-low latency and detailed monitoring. Create and maintain evaluation datasets to test safety, prompt following … and drive to improve model performance. Why Join Us Work on real applied LLMs from training to deployment. Collaborate with an ambitious, research-informed team. Shape the next generation of AI-driven communication tools. More ❯
Lead | £100,000 + 20% Bonus | Hybrid - 2 Days per Week in London 💰 £100,000 + 20% Bonus 📍 London - 2 days a week in office 🛠️ AWS, Python, Terraform, Kubernetes, RAG/GenAI interest Here at Burns Sheehan, we're exclusively partnered with a leading UK fintech that's transforming small business lending - processing billions in decisions annually. They've built … CloudFormation) and cloud-native development. Python scripting proficiency at platform engineer level but an understanding of software development lifecycle and production system ownership. Interest or experience in GenAI infrastructure - RAG, vector databases, LLM platforms, or agent frameworks. Strong communication skills and ability to translate technical decisions into business outcomes. This role is NOT an ML Engineering position - you won't More ❯
Lead | £100,000 + 20% Bonus | Hybrid - 2 Days per Week in London 💰 £100,000 + 20% Bonus 📍 London - 2 days a week in office 🛠️ AWS, Python, Terraform, Kubernetes, RAG/GenAI interest Here at Burns Sheehan, we're exclusively partnered with a leading UK fintech that's transforming small business lending - processing billions in decisions annually. They've built … CloudFormation) and cloud-native development. Python scripting proficiency at platform engineer level but an understanding of software development lifecycle and production system ownership. Interest or experience in GenAI infrastructure - RAG, vector databases, LLM platforms, or agent frameworks. Strong communication skills and ability to translate technical decisions into business outcomes. This role is NOT an ML Engineering position - you won't More ❯
external enablement activities. Preferred qualifications Experience with full-stack ML engineering to seamlessly combine retrieval-based knowledge and generative text generation to implement and optimize RAG models using first-party and OSS models. Experience with implementing search concepts, such as indexing, scoring, relevancy, faceting, and query rewriting and expansion. Experience with semantic search frameworks and tools More ❯
AI Product Manager (RAG experience essential) - £650 per day - Outside IR35 Are you excited by the challenge of turning cutting-edge AI into real, working products? We’re looking for an experienced Product Manager to help define, deliver, and scale AI Minimum Viable Products (MVPs) for a major enterprise client - applying RAG, LLMs, and generative AI to automate workflows, enhance … high-value AI opportunities and shape product roadmaps. Translate requirements into actionable user stories and acceptance criteria for engineering and data teams. Collaborate closely with teams building solutions using RAG pipelines, LLMs, embeddings, and API integrations. Define and track KPIs such as accuracy, latency, adoption, and ROI — iterating based on real-world feedback. Ensure all solutions meet enterprise-level standards … About You Proven experience as a Product Manager in AI, data, or enterprise technology environments. Hands-on exposure to AI MVPs, LLM applications, or applied ML use cases (e.g. RAG, copilots, generative search). Strong technical grounding in vector search, embeddings, APIs, data pipelines, and prompt design. Skilled in navigating complex enterprise settings and managing senior stakeholders. Pragmatic and delivery More ❯
/CDC) and entity resolution for graph population Author complex queries (Cypher, GSQL, AQL, SPARQL etc. depending on stack) Integrate knowledge graph retrieval & reasoning into LLM/RAG/GraphRAG systems Develop and evaluate graph ML/embedding models (link prediction, anomaly detection) Optimize graph performance, scaling, and query efficiency Liaise with client stakeholders: translate business problems into … TigerGraph, ArangoDB, OrientDB, or Stardog Proficiency in query languages (Cypher, GSQL, AQL, SPARQL, etc.) Strong background in pipelines, ETL, and entity resolution Exposure to integrating KG + LLM or RAG architectures Experience with graph algorithms, embeddings, or GNNs Cloud & production engineering literacy (AWS/Azure/GCP, containerization, CI/CD) Excellent communication skills — able to explain complex graph/ More ❯
City of London, London, United Kingdom Hybrid/Remote Options
Intelix.AI
/CDC) and entity resolution for graph population Author complex queries (Cypher, GSQL, AQL, SPARQL etc. depending on stack) Integrate knowledge graph retrieval & reasoning into LLM/RAG/GraphRAG systems Develop and evaluate graph ML/embedding models (link prediction, anomaly detection) Optimize graph performance, scaling, and query efficiency Liaise with client stakeholders: translate business problems into … TigerGraph, ArangoDB, OrientDB, or Stardog Proficiency in query languages (Cypher, GSQL, AQL, SPARQL, etc.) Strong background in pipelines, ETL, and entity resolution Exposure to integrating KG + LLM or RAG architectures Experience with graph algorithms, embeddings, or GNNs Cloud & production engineering literacy (AWS/Azure/GCP, containerization, CI/CD) Excellent communication skills — able to explain complex graph/ More ❯
Do you want to build the infrastructure powering the next generation of AI agents? Have you scaled backend systems that drive automation at speed? Ready to join a profitable AI startup transforming how industries deploy technology? A profitable, fast-growing AI company is building the “roads” for AI agents — providing the infrastructure that lets businesses deploy and integrate …/LLM components into production systems. Role Breakdown: 50% Backend Engineering: FastAPI, Flask, Node.js, CI/CD 30% Data Engineering: ETL, DBT, Airflow 20% AI/LLM Integration: LangChain, RAG pipelines, orchestration Key Responsibilities: Design and build backend services to support AI agent deployment Develop scalable data pipelines and integration layers Implement AI/LLM-powered features with LangChain and … of Front-End, and adoption engineers Reporting Line: CTO Visa: Cannot sponsor Ideal Profile: 5–8 years’ experience in backend or data engineering (Python) Exposure to AI/LLMs, RAG, or personal AI side projects Experience in startups or scale-ups where you’ve helped build or grow systems Solid education and strong coding fundamentals Comfortable working in a small More ❯
Do you want to build the infrastructure powering the next generation of AI agents? Have you scaled backend systems that drive automation at speed? Ready to join a profitable AI startup transforming how industries deploy technology? A profitable, fast-growing AI company is building the “roads” for AI agents — providing the infrastructure that lets businesses deploy and integrate …/LLM components into production systems. Role Breakdown: 50% Backend Engineering: FastAPI, Flask, Node.js, CI/CD 30% Data Engineering: ETL, DBT, Airflow 20% AI/LLM Integration: LangChain, RAG pipelines, orchestration Key Responsibilities: Design and build backend services to support AI agent deployment Develop scalable data pipelines and integration layers Implement AI/LLM-powered features with LangChain and … of Front-End, and adoption engineers Reporting Line: CTO Visa: Cannot sponsor Ideal Profile: 5–8 years’ experience in backend or data engineering (Python) Exposure to AI/LLMs, RAG, or personal AI side projects Experience in startups or scale-ups where you’ve helped build or grow systems Solid education and strong coding fundamentals Comfortable working in a small More ❯
We are looking for Software Engineers to join our partner company! You will play a key role in building next-generation infrastructure and AI systems that accelerate innovation across industries such as aerospace, healthcare, and energy — fields still limited by outdated, manual processes. This is not a traditional engineering role. It’s a chance to join an early … technologies. Design and ship backend systems from day one — scalable architectures, APIs, and AI-powered tools. Build and deploy LLM-based applications using frameworks such as LangChain, semantic search, RAG, and fine-tuning (SFT/RL). Develop backend logic and data pipelines to power intelligent automation and real-time processing. Manage infrastructure: relational and graph databases, containerization, CI/… Backend, or AI Engineer. Proven track record of building and shipping production-ready systems. Hands-on experience with distributed systems, APIs, databases, and cloud infrastructure. Familiarity with LLMs, LangChain, RAG, or fine-tuning techniques (SFT/RL). Strong problem-solving and debugging abilities with a proactive, builder’s mindset. Comfortable in a fast-paced, early-stage environment where you More ❯
