At a Glance
- Tasks: Lead AI strategy and research for innovative cybersecurity solutions.
- Company: Fast-growing tech startup focused on generative AI and cybersecurity.
- Benefits: Remote work, competitive salary, and opportunities for executive ownership.
- Other info: Collaborate with expert teams and influence next-gen AI standards.
- Why this job: Drive technical innovation and shape the future of AI in cybersecurity.
- Qualifications: 7+ years in AI/ML with expertise in LLMs and proven product delivery.
As the AI Director at a fast-growing, well-funded technology startup, you will lead the strategy, research, and implementation of the company’s AI initiatives, driving the technical innovation behind its generative AI-powered cybersecurity solutions. Reporting directly to executive engineering leadership as part of the broader leadership team, you will own the end-to-end AI strategy—from identifying breakthrough research directions to developing prototypes and guiding their integration into customer-facing products. This is a senior leadership opportunity at the intersection of generative AI and cybersecurity.
You will take full ownership of the AI function from day one, managing an existing AI team and overseeing research direction, evaluation systems, and prototype development while scaling the organization’s overall AI capabilities. Success in this role will be measured by roadmap progress, quantifiable improvements in model and agent performance, and the ability to drive competitive differentiation through AI innovation. This role is ideal for a senior AI researcher/engineer with deep experience in LLMs and agent systems who has shipped real product features and is eager to define the AI foundation of a next-generation cybersecurity company.
Key Responsibilities
- Lead AI Strategy and Research Direction: Own the technical AI roadmap, proactively identifying and validating novel research directions that strengthen cybersecurity capabilities and competitive positioning.
- Drive Innovation Through Prototyping: Design and build end-to-end prototypes of new AI techniques, acting as a technical product owner to guide engineering teams toward productionizing breakthrough capabilities.
- Create robust evaluation frameworks for agent performance, fine-tuning results, and system-level AI capabilities to ensure consistent, measurable improvements.
- Manage and Scale the AI Team: Provide direct leadership, mentorship, and prioritization for the AI team, establishing processes that enable rapid research progress and high-quality output.
- Ensure Technical Excellence: Collaborate closely with engineering teams to review system architecture, inform design decisions, and uphold high standards across all AI and agent-based components.
- Partner with Engineering Leadership: Translate research concepts into product features that drive customer value in close collaboration with senior engineering leadership.
- Stay at the Research Frontier: Continuously monitor cutting-edge work in LLMs, agents, and related fields, converting research insights into actionable roadmap items.
Qualifications
- Deep LLM/Generative AI Expertise: 7+ years in AI/ML with 3+ years focused on LLMs, generative AI, and agent systems, including hands-on experience with transformer architectures, fine-tuning, and production deployments.
- Proven Product Delivery Experience: Demonstrated success shipping AI/LLM features in production environments at product-focused organizations.
- Senior Technical Leadership Experience: Experience leading AI research and development teams and delivering on technical roadmaps at high-performing technology organizations.
- Research + Implementation Strength: Comfortable moving between cutting-edge research, prototype development, and guiding production implementation.
- Strategic Product Thinking: Ability to apply AI techniques to real-world challenges and define the metrics and benchmarks that drive system performance.
- Team Leadership and Development: Track record of managing, mentoring, and growing high-performing technical teams.
- Strong Technical Architecture Skills: Solid software engineering fundamentals and experience designing scalable AI systems.
Nice to Have
- Experience with NLP and agent frameworks (e.g., LangChain, LlamaIndex, etc.)
- Cybersecurity domain knowledge
- Experience in AI-focused startups, especially scaling research into product impact
- Advanced research background (e.g., PhD) with publications
- Familiarity with AI safety, evaluation methodologies, and responsible deployment
- Experience building custom benchmarking and evaluation systems
Why This Role Matters
- Lead Technical Innovation: Own the AI strategy at a company building novel applications of LLMs and agents to solve major cybersecurity challenges.
- Translate cutting-edge research into real product capabilities with visible impact for customers.
- Collaborate With Expert Teams: Work with experienced engineering leaders in AI and cybersecurity, enabling both deep technical exploration and scalable execution.
- Define the AI-Native Future of Security: Shape the technical standards and architectures that will influence the next generation of AI-powered cybersecurity.
- Clear path to expanded executive ownership, with the opportunity to build transformative AI capabilities from research through production at a fast-growing startup.
Director of AI | Machine Learning | Deep Learning | Natural Language Processing | Large Languag[...] employer: Enigma
Enigma is an exceptional employer that fosters a dynamic and innovative work culture in the heart of London. With a strong focus on employee growth, we offer opportunities for professional development and hands-on leadership in cutting-edge AI technologies. Our commitment to creating explainable AI solutions in healthcare not only drives meaningful impact but also ensures that our team members are at the forefront of technological advancements in a supportive and collaborative environment.
StudySmarter Expert Advice🤫
We think this is how you could land Director of AI | Machine Learning | Deep Learning | Natural Language Processing | Large Languag[...]
✨Join Local Tech Meetups
Get out there and mingle with fellow developers by joining local tech meetups. It’s a fantastic way to meet people who might be working at Enigma or know someone who does. Plus, you can pick up some trendy tech skills and trends while you're at it!
✨Contribute to Open Source Projects
Show off your coding chops by jumping into open-source projects. Not only does this give you practical experience, but it also gets you noticed in the dev community. You'll create a killer portfolio that speaks volumes about your skills to Enigma.
✨Tap into Online Developer Communities
Don’t underestimate the power of online developer communities like GitHub, Stack Overflow, and even Reddit. Participate in discussions, share your projects, and build your visibility. We can often find opportunities through these channels that can lead to a full-time gig at companies like Enigma.
✨Explore Job Boards Specifically for Tech Roles
Keep your eyes peeled on job boards that focus on tech roles. Sites like TechCareers or Stack Overflow Jobs can often have listings for companies like Enigma that might not show up on broader job sites. Make it a habit to check these regularly, and don’t hesitate to apply directly through our website!
We think you need these skills to ace Director of AI | Machine Learning | Deep Learning | Natural Language Processing | Large Languag[...]
Some tips for your application 🫡
Show off your coding skills:When applying for a software engineering role, it's super important to showcase your coding skills. Make sure your CV includes your tech stack, any relevant programming languages you’re comfortable with, and examples of projects you've worked on. If you have a GitHub profile, link it up! We love to see code in action.
Tailor your portfolio:For a full-time role, we’d expect to see some solid examples of your work in your portfolio. Make sure to include at least two or three projects that highlight your problem-solving skills and your ability to work with different technologies. Focus on the projects that are most relevant to the position at Enigma.
Craft a killer cover letter:Your cover letter is your chance to stand out—make it personal! Explain why you want to work at Enigma and how your skills align with the role. Show us your passion for software development. We dig enthusiastic candidates who understand the value of collaboration and continuous learning!
Be clear and concise:When it comes to writing your CV and cover letter, clarity is key. Avoid jargon that could confuse us and stick to simple, direct language. Highlight your achievements with quantifiable results where possible, and keep everything easy to read. A well-organised application goes a long way!
How to prepare for a job interview at Enigma
✨Brush Up on Your Coding Skills
For a full-time software engineering role, it's crucial that we stay sharp with our coding abilities. Expect technical questions that might involve solving problems on the spot or discussing algorithms. Practise on platforms like LeetCode or HackerRank to get comfortable with the types of questions that often come up.
✨Know Your Tools and Frameworks
Make sure we’re well-acquainted with the tools and technologies listed in the job description. Familiarise ourselves with any specific frameworks or programming languages mentioned. If Enigma uses React or Node.js, for instance, be ready to discuss how we’ve used them in previous projects or coursework.
✨Showcase Your Projects
Bring along a portfolio that highlights our best work. This could be code samples, GitHub repositories, or any side projects we’ve built. Make sure we can talk through our thought process for each project, especially the challenges we faced and how we solved them—this shows our problem-solving skills in action.
✨Prepare for Behavioural Questions
While technical skills are key, full-time positions also require cultural fit. Be ready to discuss our previous experiences and how we handle teamwork, conflict, and deadlines. Brush up on the STAR method—Situation, Task, Action, Result—to clearly articulate our past experiences when discussing how we've contributed to a team.