Senior AI Engineer

As a Senior AI Engineer at Factored, you will engage full-time in designing and building production-grade GenAI systems that transform enterprise AI strategies into secure, scalable applications. You are a hands-on engineer working directly with client teams, shipping production code while contributing to technical strategy and learning from experienced leaders. You own complex, high-impact projects from architecture through optimization and deployment. This role exists for engineers who want to build at the frontier of AI while growing their technical expertise.

Functional Responsibilities:

Architect, design, and implement backend systems that integrate with LLMs, and ensure services are scalable, secure, and production-ready

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 and build 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.

Collaborate with ML Engineers, product managers, and cross-functional teams to understand requirements and deliver AI features that drive measurable value.

Communicate technical architecture and trade-offs clearly to stakeholders at all levels.

Contribute to raising engineering standards through code review and knowledge sharing.

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.

Comfortable translating between technical and business perspectives and managing ambiguous requirements.

A growth mindset and genuine interest in learning and applying new AI/ML techniques to real-world challenges.