At a Glance
- Tasks: Design and build AI applications that enhance productivity using cutting-edge technology.
- Company: Join a forward-thinking tech company focused on AI innovation.
- Benefits: Enjoy unlimited vacation, competitive salary, and comprehensive health insurance.
- Other info: Hybrid work environment with opportunities for professional growth.
- Why this job: Make a real impact by developing AI solutions that empower teams and clients.
- Qualifications: Strong Python skills and experience with LLM APIs are essential.
The predicted salary is between 60000 - 80000 £ per year.
We are seeking an exceptional AI Engineer to join our growing team and take a leading role in building and evolving our internal AI‑powered agent platform used across the organization. In this role, you will design, build, and improve AI‑powered applications that integrate large language models (LLMs) with internal tools, SQL databases, advertising APIs (Google Ads, Meta), and stateful code execution environments. You will play a critical role in improving reliability, context orchestration, evaluation maturity, and expanding Rai into document and workflow automation use cases. Your work will directly impact 200–500 employees and potentially clients, making AI a core productivity multiplier across the organization.
Responsibilities
- Design, build, and maintain AI‑powered applications leveraging LLM APIs, tool calling, and structured outputs.
- Develop and improve agent architectures that integrate tools such as SQL execution, Python code execution, and marketing platform APIs through MCP‑style integrations.
- Design and refine multi‑turn context handling, session memory strategies, and prompt structures to improve response reliability and reduce hallucinations.
- Implement safeguards for tool execution, SQL queries, and code execution. Improve retry logic, structured validation, and hallucination mitigation strategies.
- Contribute to building structured evaluation frameworks for LLM‑powered systems, including multi‑turn testing, regression testing, and task‑based success metrics.
- Support expansion of Rai into document generation, tables, slide decks, and potential Google Workspace integrations.
- Work closely with Machine Learning Engineers, Product Managers, and Full‑Stack Engineers to ensure AI systems are robust, maintainable, and aligned with business goals.
- Stay current with advances in LLM systems, agent architectures, and applied AI tooling to continuously improve Rai’s capabilities.
Qualifications
- Strong programming skills in Python with experience building production systems.
- Experience building applications powered by LLM APIs (OpenAI, Anthropic, Vertex AI, etc.).
- Deep understanding of context windows and token limits, multi‑turn conversation behavior, tool/function calling, structured outputs (JSON schema), and prompt design and failure modes.
- Experience integrating external APIs into production systems.
- Proficiency in SQL.
- Experience working with cloud platforms (GCP, AWS, or Azure).
- Strong problem‑solving skills and ability to debug complex system behavior.
- Excellent communication and cross‑functional collaboration skills.
Strong Plus
- Experience designing evaluation frameworks for LLM‑powered systems.
- Understanding of embeddings, vector search, and RAG architectures.
- Experience building agent or multi‑agent workflows.
- Experience with stateful execution environments or sandboxed code execution.
- Experience with document automation (Google Docs, Slides, PDFs).
- Experience building internal developer tools or productivity platforms.
Required Knowledge
- Python
- SQL
- Cloud platforms (GCP, AWS, Azure)
- LLMs / AI APIs
- Git / GitHub
Nice to Have
- Data warehouses (BigQuery, Snowflake, Redshift)
- Data transformation (dbt)
- Semantic layers (Cube, Looker, dbt Metrics)
- TypeScript
Benefits
- Competitive salary and benefits based on ability level.
- Unlimited vacation policy.
- Monthly phone stipend.
- Comprehensive medical, dental, and vision insurance options.
- 401(K) plan with matching.
- Dog friendly office.
- Hybrid work opportunity.
- Professional development program.
- Bonus perk: seamless allowance.
Locations
- New York City: 43-01 22nd St, Suite 602, Queens, NY 11101, United States
- Bogotá: WeWork Av. Carrera 19 #100-45 Usaquén, Piso (Floor) 10, Bogotá, Distrito Capital de Bogotá 110111, Colombia
- Mexico City: Av. Insurgentes Sur 1082, Piso (Floor) 2, Oficina 2008, Ciudad de México, CDMX 03100, México
AI Engineer employer: twentysix
Join our dynamic team as an AI Engineer in the vibrant city of New York, where you'll have the opportunity to shape the future of AI-powered applications that enhance productivity across the organisation. We offer a competitive salary, unlimited vacation, and a hybrid work environment, fostering a culture of innovation and collaboration. With a strong focus on professional development and a dog-friendly office, we are committed to supporting your growth while making a meaningful impact on our clients and employees alike.
StudySmarter Expert Advice🤫
We think this is how you could land AI Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with potential colleagues on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your AI projects, especially those involving LLMs and APIs. This gives us a tangible way to see what you can do and how you think, making you stand out from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on common AI engineering questions and coding challenges. Practice explaining your thought process clearly, as communication is key when collaborating with cross-functional teams.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining our team at StudySmarter.
We think you need these skills to ace AI Engineer
Some tips for your application 🫡
Tailor Your Application:Make sure to customise your CV and cover letter for the AI Engineer role. Highlight your experience with LLM APIs, Python programming, and any relevant projects that showcase your skills. We want to see how you fit into our team!
Showcase Your Projects:Include links to any projects or applications you've built that demonstrate your expertise in AI and programming. This could be GitHub repositories or live demos. It helps us see your practical skills in action!
Be Clear and Concise:When writing your application, keep it straightforward and to the point. Use clear language to describe your experiences and achievements. We appreciate clarity and want to understand your journey without sifting through fluff.
Apply Through Our Website:Don’t forget to submit your application through our website! It’s the best way for us to receive your details and ensures you’re considered for the role. Plus, it makes the process smoother for everyone involved.
How to prepare for a job interview at twentysix
✨Know Your Tech Inside Out
Make sure you’re well-versed in Python and the LLM APIs mentioned in the job description. Brush up on your SQL skills too, as you'll likely be asked to demonstrate your understanding of integrating these technologies during the interview.
✨Showcase Your Problem-Solving Skills
Prepare to discuss specific examples where you've tackled complex system behaviours or debugging challenges. Use the STAR method (Situation, Task, Action, Result) to structure your answers and highlight your problem-solving prowess.
✨Understand the Role's Impact
Be ready to explain how your work as an AI Engineer can directly influence productivity across the organisation. Think about how you can articulate the value of AI-powered applications in improving workflows and automating tasks.
✨Stay Current with AI Trends
Familiarise yourself with the latest advancements in LLM systems and agent architectures. Being able to discuss recent developments or tools will show your passion for the field and your commitment to continuous learning.
