HELLO,
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Applied AI Engineer  I am working with a fast growing AI company building an enterprise grade AI workspace used by major financial institutions to produce and validate client ready work. The platform replaces complex manual workflows with automated AI systems that scale across global teams and has grown rapidly with backing from top tier investors. This role is for engineers who want to build and ship production systems. You will own core parts of the AI agent infrastructure, including multi agent systems, RAG pipelines, and evaluation frameworks. The work is hands on and production focused, covering backend services, AI infrastructure, and delivery at scale. What you will doBuild and deploy backend services and APIs, Python preferred using Django or FastAPIProductionise AI features including RAG, agent orchestration, and evalsCreate data pipelines for training, evaluation, and continuous improvementEnsure performance, reliability, and security across the stackWork closely with founders, engineers, and product teamsWhat we are looking forFive plus years of software engineering experienceProven experience deploying AI applications into productionStrong backend engineering skills and database fundamentalsExperience with cloud infrastructure, Docker, Kubernetes, and CI CDBackground workers, task queues, and Redis experienceFamiliarity with LLM evaluation, monitoring, and safetyDegree from a Russell Group university or equivalent top tier academic background, or alternatively extensive engineering expertise with clear, relevant production experienceThis is a demanding, in office environment with high ownership, shifting priorities, and strong technical standards. You will work directly with founders who have built and exited venture backed companies. If you are an Applied or Agentic AI Engineer looking for real ownership and the chance to build core systems from the ground up, this is worth a conversation.

I am working on a Lead AI Developer role for a UK based team delivering AI solutions into complex, non technical environments. This is a hands on role for someone who codes daily but also leads from the front. You would sit between senior stakeholders and delivery teams, shaping requirements, explaining trade offs, and guiding technical direction without relying on formal authority. What the role actually needs. You are still a builder. Strong in C#, .NET, and Python, comfortable shipping production systems, deploying to cloud, and working with modern AI patterns like LLMs, RAG, and agent based workflows. At the same time, you are confident in front of clients. You can run a requirements session, challenge vague asks, surface constraints early, and translate technical decisions into language that non engineers trust. You have led delivery through influence. Mentoring developers, setting standards, steering architecture discussions, and handling competing priorities when stakeholders want different outcomes. What you would be doingWorking directly with clients to turn real world problems into clear technical designs and delivery plansLeading backlog refinement, sprint planning, and technical prioritisationBuilding and deploying AI enabled features across a Microsoft and Azure focused stackExplaining feasibility, risk, and trade offs in a way that helps stakeholders make decisionsRaising the bar for engineering quality through reviews, coaching, and exampleWhat tends to work well herePeople who have been the technical lead in client facing environmentsDevelopers who enjoy ambiguity and creating clarity rather than waiting for perfect specsEngineers who can say no when needed, and explain why in a constructive wayIf you are interested in this position, feel free to send your updated CV and we'll be in touch if this is a match.

Deep Learning Engineer – Advanced Robotics & VLM/VLALocation: Flexible / Remote (UK or Europe preferred) Employment Type: Full-timeAbout the CompanyOur client is an ambitious AI and robotics company developing next-generation humanoid systems designed to transform how intelligent automation supports industrial and everyday environments. Their mission is to advance human potential through robotics that are scalable, safe, and capable of performing complex real-world tasks. This is a rare opportunity to work at the intersection of deep learning, multimodal AI, and robotic embodiment, helping shape the foundations of a truly intelligent automation platform. The Role As a Deep Learning Engineer, you’ll design and train large-scale models that power robotic control and perception — from foundational representation learning to behaviour cloning and reinforcement learning. You’ll work across the full data-to-deployment lifecycle, experimenting with cutting-edge multimodal architectures and building robust pipelines for high-performance, real-time systems. Key ResponsibilitiesDevelop and train deep learning models for manipulation, navigation, and general policy learning.Collaborate with teleoperations and simulation teams to define data collection goals and bridge the sim-to-real gap.Train and fine-tune multimodal LLMs, VLMs, and VLAs, integrating diverse sensory modalities (vision, audio, proprioception, LiDAR, etc.).Build scalable data pipelines for continuous ingestion, curation, weak supervision, and retraining.Partner with MLOps and infrastructure teams to enable distributed training and optimize models for real-time deployment.Contribute to shaping the next generation of embodied AI systems for safe, efficient automation.About You3 years of experience building and deploying deep learning systems (industry or research).Strong proficiency in Python and PyTorch or JAX.Hands-on experience with LLMs, VLMs, or generative models for image/video.Deep understanding of training infrastructure (streaming datasets, checkpointing, distributed compute).Strong communicator with clear experiment documentation and the ability to explain complex technical decisions.Bonus PointsExperience in robotics, autonomous driving, or other embodied AI domains.Background in reinforcement learning (PPO, DPO, SAC, etc.) or RL for LLMs.Experience optimizing deep nets for production (latency, telemetry, on-device inference).Publications at top-tier ML conferences (ICLR, NeurIPS, ICML) or significant open-source contributions.Familiarity with OpenVLA, π models, or similar embodied AI frameworks.What’s on OfferCompetitive compensation including stock options.Flexible remote-first setup with opportunities for international collaboration.Work with world-class researchers and engineers building truly transformative technology.A fast-paced, innovation-driven culture where ideas move quickly from concept to prototype.

Lead Speech Research Scientist (ASR/TTS) London preferred | Remote considered | Visa sponsorship availableWe’re collaborating with a growing AI company developing advanced conversational technologies that power intelligent voice and chat systems for global enterprises. This is a senior research and leadership role for someone with deep expertise in ASR, TTS, and related speech–language technologies. You’ll head a high-impact research group, working directly with company leadership to define research priorities, guide technical direction, and translate innovations into production.Key Responsibilities:Lead applied research and development in speech recognition, speech synthesis, and conversational AI.Define the research roadmap and ensure alignment with strategic and product goals.Mentor and grow a team of researchers, fostering collaboration and innovation.Translate real-world product challenges into clear research objectives and milestones.Oversee data strategy, evaluation metrics, and system performance.Collaborate closely with product and engineering teams to deploy research outcomes.Represent the team externally through papers, conferences, and community engagement.What We’re Looking ForPhD (preferred) or Master’s in Speech Technology, Computer Science, or related field.Proven expertise in Automatic Speech Recognition (ASR) and/or Text-to-Speech (TTS).3 years of applied research experience in speech or language AI (industry or academic).2 years leading or mentoring research teams or projects.Strong programming skills in Python, with deep learning experience using PyTorch.Practical experience taking models from research to production environments.Bonus SkillsExperience with multilingual or low-resource speech systems.Familiarity with LLM integration into conversational pipelines.Strong engineering foundations (Git, Docker, CI/CD).Publications in major speech or AI conferences (e.g., Interspeech, ICASSP, NeurIPS).This is a key hire in a company entering an exciting growth phase — an opportunity to shape the future of speech and conversational AI while leading a talented, expanding research team.