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MongoDB Engineer A leading global Aviation and Transport IT Solutions Provider is looking for a strong, senior MongoDB Engineer to join their team based in London. Your work will include query optimisation and conversion of queries into MongoDB. Someone with MongoDB development and administration experience would be ideal. * MongoDB Certificate is essential - please do not apply if you don't have one * Responsibilities: Use your strong interpersonal, analytical and problem-solving skills towards all aspects of MongoDB architecture and administration along with Linux OS and Cloud administration. Important experience: Strong understanding of MongoDB architecture, including sharding, replication and indexing. Proficiency with Linux systems and Shell Scripting. Familiarity with monitoring tools and performance tuning techniques. Experience with disaster recovery plans is a plus Proven experience working with cloud platforms, VM management in the cloud (preferable AWS) Hands on experience in use and administration of Linux OS Requirements: Bachelor's Degree in Information Technology, Computer Science or equivalent Minimum 2 years of proven experience with Mongo Atlas and/or self-managed MongoDB clusters Minimum 4 years Mongo DBA experience MongoDB certified.* this is essential.* Strong written and verbal English Tech Stack Java, Angular, Ionic, Spring Boot, REST APIs and Mongo DB Linux, Git, SVN, Ansible & Maven CI/CD with Gitlab/Bamboo Agile-Scrum Compensation and Location: Competitive salary and package Remote/Hybrid 1-5 days per month in the office Office Location: West London, near Heathrow Airport

Role Overview As Head of AI, you will be the primary technical driver of all AI/ML initiatives. You'll report directly to the CEO/CTO and own the full life cycle of our AI roadmap-from research and proof-of-concept to scalable production. We're looking for a doer who can rapidly prototype models, optimize for performance, and mentor junior engineers, all while helping define product strategy. In this role, you will: Lead AI strategy and execution in a high-ambiguity environment. Build, train, and deploy state-of-the-art models (eg, deep learning, NLP, computer vision, reinforcement learning, or relevant domain-specific architectures). Design infrastructure for data ingestion, annotation, experimentation, model versioning, and monitoring. Collaborate closely with product, design, and DevOps to integrate AI features into our platform. Continuously evaluate new research, open-source tools, and emerging frameworks to keep us at the forefront. Recruit, mentor, and grow an AI/ML team as we scale beyond our seed round. Key Responsibilities 1. Architecture & Hands-On Development Define and implement end-to-end AI pipelines: data collection/cleaning, feature engineering, model training, validation, and inference. Rapidly prototype novel models (eg, neural networks, probabilistic models) using PyTorch, TensorFlow, JAX, or equivalent. Productionize models in cloud/on-prem environments (AWS/GCP/Azure) with containerization (Docker/Kubernetes) and ensure low-latency, high-availability inference. 2. Strategic Leadership Develop a multi-quarter AI roadmap aligned with product milestones and fundraising milestones. Identify and evaluate opportunities for AI-driven competitive advantages (eg, proprietary data, unique model architectures, transfer/few-shot learning). Collaborate with business stakeholders to translate big problems into technically feasible AI solutions. 3. Data & Infrastructure Oversee the creation and maintenance of scalable data pipelines (ETL/ELT) and data lakes/warehouses. Establish best practices for data labeling, versioning, and governance to ensure high data quality. Implement ML Ops processes: CI/CD for model training, automated testing, model-drift detection, and continuous monitoring. 4. Team Building & Mentorship Hire and mentor AI/ML engineers, data scientists, and research interns. Set coding standards, model-development guidelines, and rigor around reproducible experiments (eg, clear Git workflow, experiment tracking). Conduct regular code/model reviews and foster a culture of learn by doing and iterative improvement. 5. Research & Innovation Stay abreast of state-of-the-art AI research (eg, pre-training, fine-tuning, generative methods) and evaluate applicability. Publish or present whitepapers/prototype demos if appropriate (keeping stealth constraints in mind). Forge partnerships with academic labs or open-source communities to accelerate innovation. Minimum Qualifications Experience (7 + years total; 3 + years in senior/lead role): Demonstrated track record of shipping AI/ML products end-to-end (from prototype to production). Hands-on expertise building and deploying deep learning models (eg, CNNs, Transformers, graph neural networks) in real-world applications. Proficiency in Python and core ML libraries (PyTorch, TensorFlow, scikit-learn, Hugging Face, etc.). Strong software engineering background: data structures, algorithms, distributed systems, and version control (Git). Experience designing scalable ML infrastructure on cloud platforms (AWS SageMaker, GCP AI Platform, Azure ML, or equivalent). Solid understanding of data-engineering concepts: SQL/noSQL, data pipelines (Airflow, Prefect, or similar), and batch/streaming frameworks (Spark, Kafka). Leadership & Communication: Proven ability to lead cross-functional teams in ambiguous startup settings. Exceptional written and verbal communication skills-able to explain complex concepts to both technical and non-technical stakeholders. Experience recruiting and mentoring engineers or data scientists in a fast-paced environment. Education: Bachelor's or Master's in Computer Science, AI/ML, Electrical Engineering, Statistics, or a related field. (Ph.D. in AI/ML is a plus but not required if hands-on experience is extensive.) Preferred (Nice-to-Have) Prior experience in a stealth-mode or early-stage startup, ideally taking an AI product from 0 - 1. Background in a relevant domain (eg, healthcare AI, autonomous systems, finance, robotics, computer vision, or NLP). Hands-on experience with large-scale language models (LLMs) and prompt engineering (eg, GPT, BERT, T5 family). Familiarity with on-device or edge-AI deployments (eg, TensorFlow Lite, ONNX, mobile/Embedded inference). Knowledge of MLOps tooling (MLflow, Weights & Biases, Kubeflow, or similar) for experiment tracking and model governance. Open-source contributions or published papers in top-tier AI/ML conferences (NeurIPS, ICML, CVPR, ACL, etc.). Soft Skills & Cultural Fit Doer Mindset: You thrive in scrappy, ambiguous environments. You'll roll up your sleeves to write production code, prototype research ideas, and iterate quickly. Bias for Action: You favor shipping an MVP quickly, measuring impact, and iterating-over striving for perfect academic proofs that never see production. Ownership Mentality: You treat the startup as your own: you take responsibility for system uptime, data integrity, and feature adoption, not just model accuracy. Collaborative Attitude: You value cross-functional teamwork and can pivot between researcher mode and software engineer mode depending on the task at hand. Growth-Oriented: You continually learn new algorithms, architectures, and engineering best practices; you encourage team members to do the same. What We Offer Equity Package: Meaningful ownership stake, commensurate with an early leadership role. Competitive Compensation: Salary aligned with early-stage startup benchmarks; a large portion of the upside is in equity. Autonomy & Impact: You'll shape the technical direction of our AI stack and lay the groundwork for a market-leading product. Flexible Work Environment: Remote-friendly with occasional in-person retreats or team meetups. Learning Budget: Funds for conferences, courses, or publications to ensure you stay at the bleeding edge. Fast-Track Growth: As our first AI hire and eventual team leader, you'll rapidly expand your responsibilities-and the team you build-within months. How to Apply Please send your resume/CV and a brief cover letter with the subject line: Head of AI Application - [Your Name] In your cover letter, highlight: 1. A recent project where you built and deployed an AI/ML system end-to-end (include technical stack and impact). 2. Any leadership or mentoring experience guiding other engineers or data scientists. 3. Why you're excited to join a stealth startup and move quickly from prototype to production. We will review applications on a rolling basis and aim to schedule initial calls within two weeks of receipt. Equal Opportunity: We are committed to building a diverse team and welcome applicants of all backgrounds. We celebrate differences and encourage individuals who thrive in a fast-paced, collaborative, and impact-driven culture to apply. Ready to build world-class AI from day one? Come join us and help shape the future.