Senior AI Engineer
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Job Description
Senior AI Engineer building secure, scalable GenAI systems for Factored, which helps U.S. companies build AI, ML, and data teams with LATAM talent. Owning architecture, deployment, and optimization.
Responsibilities:
- Architect, design, and implement backend Python systems integrating LLMs via the Model Context Protocol (MCP), ensuring services are scalable, secure, and production-ready.
- Implement 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, including Bedrock, Lambda, and ECS.
- Champion Spec-Driven Development (SDD) and Context-Driven Development (CDD) for deterministic, structured model outputs.
- Architect semantic memory layers and build advanced RAG pipelines using vector databases.
- Implement AI security guardrails, boundary controls, and Human-in-the-Loop approval flows for compliance, privacy, and safety.
- Monitor application health with APM, LangSmith, and Phoenix; track agent trajectories and LLM performance metrics and resolve performance issues.
- Onboard teams, build reusable prompts, automate repetitive workflows, and drive organization-wide adoption of AI-native developer tools such as Claude Code and Cursor.
- Collaborate with ML Engineers, product managers, and cross-functional teams to understand requirements and deliver valuable AI features.
- Communicate technical architecture and trade-offs to stakeholders.
- Raise engineering standards through code review and knowledge sharing.
Requirements:
- 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 (LangGraph, LangChain, AutoGen) and agentic state management.
- Practical experience with the Model Context Protocol (MCP), function calling, tool-use APIs, and enforcing structured outputs across major foundation models.
- Experience with cloud platforms like AWS, including Bedrock, building, deploying, and managing cloud-native applications; exposure to Azure or GCP is also valuable.
- Experience designing and developing APIs using frameworks like FastAPI or Django to wrap agentic workflows into consumable backend services.
- Strong system design and problem-solving skills, capable of visualizing and debugging complex, non-linear agent trajectories and AI workflows.
- Practical experience with databases (PostgreSQL or NoSQL) and vector databases (Pinecone, Qdrant, Milvus) for semantic memory and RAG workflows.
- Proficiency with DevOps and CI/CD tools such as Docker, Kubernetes, Terraform, and Git-based workflows.
- 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 methodologies, like SDD and CDD, to real-world challenges.
Benefits:
- Ownership through equity participation.
- Annual company retreat.
- Education bonus for continuous learning.
- Company-wide winter break.
- Paid time off.
- Optional in-person events and meetups.
- Tailored career roadmaps.
- High-performance culture.
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