F

Factored

End-to-end AI talent augmentation services

Senior AI Engineer

AI Application Engineering +1Confirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • At least 5 years of professional experience as a Software Engineer or in a related role, with a strong foundation in Python.
  • Hands-on experience with modern generative AI orchestration frameworks such as LangGraph, LangChain, and AutoGen, and with agentic state management.
  • Practical experience with the Model Context Protocol, function calling, tool-use APIs, and enforcing structured outputs across major foundation models.
  • Experience with cloud platforms such as AWS, including Bedrock, and building, deploying, and managing cloud-native applications; Azure or GCP exposure is valuable.
  • Experience designing and developing APIs using frameworks such as FastAPI or Django to wrap agentic workflows into backend services.
  • Strong system design and problem-solving skills, including the ability to visualize and debug complex, non-linear agent trajectories and AI workflows.
  • Practical experience with databases such as PostgreSQL or NoSQL and vector databases such as Pinecone, Qdrant, or Milvus for semantic memory and retrieval-augmented generation workflows.
  • Proficiency with DevOps and CI/CD tools such as Docker, Kubernetes, Terraform, and Git-based workflows.
  • Excellent written and spoken English communication skills for collaborating with global teams and explaining complex AI concepts.
  • Ability to translate between technical and business perspectives and manage ambiguous requirements.
  • Growth mindset and genuine interest in learning and applying new AI methodologies, such as Spec-Driven Development and Context-Driven Development, to real-world challenges.

Responsibilities

  • Architect, design, and implement backend Python systems that integrate with large language models via the Model Context Protocol, ensuring services are scalable, secure, and production-ready.
  • Implement complex, stateful agentic architectures and reasoning loops for multi-step AI workflows using frameworks such as LangGraph.
  • Build, deploy, and manage cloud-native AI applications on AWS services including Bedrock, Lambda, and ECS, and other modern infrastructure.
  • Champion Spec-Driven Development and Context-Driven Development to ensure models produce deterministic, highly structured outputs.
  • Architect semantic memory layers and build advanced retrieval-augmented generation pipelines using vector databases to optimize performance.
  • Implement AI security guardrails, strict boundary controls, and Human-in-the-Loop approval flows to ensure compliance, privacy, and safety.
  • Monitor application health with observability tools such as APM, LangSmith, and Phoenix; track agent trajectories and large language model performance metrics, and proactively resolve performance issues.
  • Onboard teams, build reusable prompts, and automate repetitive workflows to drive organization-wide adoption of AI-native developer tools such as Claude Code and Cursor.
  • Collaborate with machine learning engineers, product managers, and cross-functional teams to understand requirements and deliver AI features that drive measurable value.
  • Communicate technical architecture and trade-offs to stakeholders at all levels.
  • Raise engineering standards through code review and knowledge sharing, treating prompts and context strictly as code.

Desired Qualifications

  • Exposure to Azure or Google Cloud Platform.

About the Company

Factored

End-to-end AI talent augmentation services

Factored provides end-to-end talent augmentation for AI, ML, and data science by sourcing, vetting, training, and managing remote engineering teams that integrate as extensions of a client's team. Their engineers, mainly from Latin America, work across all US time zones and can provide technical leadership, hands-on execution, or advisory support to accelerate AI roadmaps for US-based companies. They organize experts into Centers of Excellence (AI/ML, Data Science, Data Analytics, Data Engineering, Software Engineering) and apply projects like personalized recommendations, fraud detection, credit risk models, and automated data pipelines. Their differentiation comes from focusing exclusively on AI/data, using a vetted upskilling program developed with Stanford and deeplearning.ai, and offering nearshore talent with seamless integration to clients, with a goal to help organizations scale AI/data capabilities quickly and cost-effectively.

Company size
201-500
Company stage
Seed
Total funding
$2.5M
Headquarters
Mountain View, CA
Founded
2019

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