Senior AI Engineer

Factor Funding Company

. Architect, design, and implement backend Python systems integrating LLMs via the Model Context Protocol (MCP), ensuring services are scalable, secure, and production-ready.

Posted 10/31/2026full-timeRemoteSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in architecting and implementing scalable backend Python systems, integrating LLMs, and managing cloud-native AI applications on AWS. Proficient in applying Spec-Driven Development and Context-Driven Development methodologies to ensure structured model outputs and compliance.

Highest-signal resume keywords

Python ProgrammingGenerative AI Orchestration FrameworksAWS Cloud ServicesAPI Development with FastAPI or DjangoDevOps and CI/CD Tools

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Hard Skills

Python ProgrammingGenerative AI Orchestration FrameworksModel Context Protocol (MCP)API Development with FastAPIDatabase Management (PostgreSQL, NoSQL)Vector Databases (Pinecone, Qdrant, Milvus)Cloud-Native Application DevelopmentSystem Design and Problem-SolvingDevOps PracticesCI/CD Tools

Soft Skills

Excellent English CommunicationCollaboration with Global TeamsTranslating Technical ConceptsGrowth MindsetManaging Ambiguous Requirements

Tools & Technologies

AWS BedrockAWS LambdaAWS ECSDockerKubernetesTerraformLangGraphLangChainAutoGenLangSmith

Industry Keywords

AI WorkflowsStateful Agentic ArchitecturesSpec-Driven Development (SDD)Context-Driven Development (CDD)Human-in-the-LoopCompliance and PrivacyPerformance MetricsApplication Health MonitoringReinforcement LearningSemantic Memory Layers

Tech Stack

Tools & technologies

AWSAzureCloudDjangoDockerGoogle Cloud PlatformKubernetesNoSQLPostgresPythonTerraform

About the role

Key responsibilities & impact

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

What you’ll need

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

Comp & perks

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.