Senior Machine Learning Engineer – LLMs, Agentic Workflows

Factor Funding Company

. Architect agent task decomposition into actionable sub-tasks .

Posted 10/31/2026full-timeRemoteSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in architecting intelligent automation systems, integrating Generative AI frameworks, and implementing agentic architectures. Proficient in developing machine learning models and deploying them in production environments while ensuring system efficiency and safety.

Highest-signal resume keywords

Machine Learning Model DevelopmentGenerative AI IntegrationAgentic Architecture DesignCloud Platform DeploymentSystem Design and API Integration

ATS Keywords

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Hard Skills

Data StructuresAlgorithmsObject-Oriented ProgrammingAPI DesignPrompt EngineeringError HandlingSelf-CorrectionMonitoring and VisualizationEvaluation FrameworksTask Decomposition

Tools & Technologies

LangChainLangGraphOpenAIClaudeLangSmithArize PhoenixPineconeMilvusQdrantAWS

Industry Keywords

Intelligent AutomationHuman-in-the-LoopMulti-Agent WorkflowsRetrieval-Augmented GenerationLong-Term Agent Memory

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformPythonReact

About the role

Key responsibilities & impact

Architect agent task decomposition into actionable sub-tasks
Develop Plan-and-Execute and ReAct patterns
Design short-term memory systems for long-running tasks
Create interfaces between LLMs and external software, databases, and APIs
Standardize function calling, legacy-system interaction, and sandboxed Python execution
Implement error handling and self-correction
Build evaluation frameworks measuring trajectory success, efficiency, and safety
Set up monitoring to visualize agent processes and diagnose workflow failures
Implement guardrails and Human-in-the-Loop checkpoints for high-stakes decisions
Own intelligent automation systems end-to-end from prototyping through production deployment

Requirements

What you’ll need

5+ years of hands-on experience developing and deploying machine learning models in production environments
Strong software engineering fundamentals, including data structures, algorithms, system design, OOP, and API design & integration
Proven experience designing and implementing agentic architectures, including multi-agent workflows, tool-calling, state management, and human-in-the-loop patterns
Expertise in integrating Generative AI frameworks and APIs such as LangChain, LangGraph, OpenAI, and Claude into production-grade applications
Strong understanding of LLM fundamentals, systematic prompt engineering (chain-of-thought, few-shot), and debugging tools like LangSmith or Arize Phoenix
Experience with vector databases such as Pinecone, Milvus, and Qdrant for retrieval-augmented generation (RAG) and long-term agent memory
Experience with cloud platforms such as AWS, GCP, or Azure for deploying AI workloads

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