Factored was conceived in Palo Alto, California by Andrew Ng and a team of highly experienced AI researchers, educators, and engineers to help address the significant shortage of qualified AI & Machine-Learning engineers globally. We know that exceptional technical aptitude, intelligence, communication skills, and passion are equally distributed around the world, and we are very committed to testing, vetting, and nurturing the most talented engineers for our program and on behalf of our clients.
We're looking for an experienced engineer with strong Python expertise who’s excited to apply their skills to the new frontier of Generative AI and agentic systems. You’ll join an ML-intensive environment to build and optimize scalable AI applications, enhance security guardrails, and design cloud-native architectures that power enterprise-grade AI solutions.
In this role, you’ll collaborate with ML engineers, data specialists, and product teams to bring AI capabilities into production—bridging the gap between intelligent models and reliable software systems. If you thrive on designing robust, cloud-native systems and are passionate about applying your backend engineering expertise to complex, probabilistic AI workflows, this is your opportunity to help define the next generation of enterprise AI.
Functional Responsibilities:
- Architect, design, and implement backend systems and APIs integrating GenAI technologies.
- Implement agentic architectures for complex AI workflows.
- Build, deploy, and manage cloud-native AI applications on AWS (Lambda, ECS, SageMaker) and other modern infrastructure.
- Fine-tune prompts, context windows, and RAG pipelines to optimize performance.
- Implement AI security guardrails and conduct risk assessments to ensure compliance, privacy, and safety.
- Monitor application health with observability tools (APM, logging, LLM performance metrics) and proactively resolve performance issues.
- Work closely with ML Engineers to consume and integrate trained models into production systems.
- Maintain high engineering standards: modular code, automated testing, CI/CD pipelines, and documentation.
- Partner with product managers, designers, and cross-functional teams to deliver user-facing AI features that drive measurable value.
Qualifications:
- 5+ years of professional experience as a Software Engineer or in a related role, with a strong foundation in Python.
- Hands-on experience or strong interest in Generative AI frameworks such as LangGraph, LangChain, OpenAI, and RAG implementations.
- Experience with cloud platforms like AWS, including building, deploying, and managing cloud-native applications; exposure to Azure or GCP is also valuable.
- Experience designing and developing APIs using frameworks like FastAPI, Django, or Flask; willingness to learn best practices for scalable production systems is appreciated.
- Strong system design and problem-solving skills, including building, testing, and optimizing backend systems or AI workflows.
- Practical experience with databases (PostgreSQL or NoSQL), and vector databases for RAG workflows.
- Proficiency with DevOps and CI/CD tools such as Docker, Kubernetes, Terraform, and Git-based workflows.
- Understanding of deep learning model development and deployment, with familiarity using frameworks like PyTorch or HuggingFace; motivated learners are welcome.
- Excellent English communication skills, both written and spoken, with the ability to collaborate effectively with global teams and explain complex AI concepts clearly.
- A growth mindset and genuine interest in learning and applying new AI/ML techniques to real-world challenges.
Top Skills
What We Do
Factored (backed by Andrew Ng's AI Fund and deeplearning.ai) helps leading tech companies select, upskill, and build world-class data science, machine learning and AI engineering teams much faster and more cost effectively. Our engineers have been personally vetted, educated, and mentored by some of the most talented and recognized AI educators and engineers from Silicon Valley, Stanford University and deeplearning.ai.
