Senior Generative AI Engineer

Belva

. Build production-grade AI applications integrating cutting-edge GenAI capabilities .

Posted 11/3/2026full-timeRemote • United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates advanced proficiency in Python and expertise in Generative AI systems, with a strong focus on prompt engineering and LLM model evaluation. Capable of building AI applications and workflows that integrate cutting-edge technologies while ensuring effective communication and collaboration.

Highest-signal resume keywords

Advanced Proficiency In PythonGenerative AI Systems ExperiencePrompt Engineering ExpertiseLLM Model Evaluation And MonitoringSynthetic Dataset Generation

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Applicant Tracking System Keywords

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Hard Skills

Software DevelopmentAI Application DevelopmentPrompt EngineeringLLM Model EvaluationSynthetic Dataset GenerationAgentic Workflow CreationContext EngineeringA/B TestingObservability PracticesOrchestration Frameworks

Soft Skills

Outstanding Communication Skills

Tools & Technologies

LangChainLangGraphAWSVoice APIsMulti-Modal LLMs

Industry Keywords

Generative AIHuman-In-The-Loop WorkflowsChat-Based UX FeaturesStartup ExperienceModel Context Protocol

Tech Stack

Tools & technologies

AWSPython

About the role

Key responsibilities & impact

Build production-grade AI applications integrating cutting-edge GenAI capabilities
Collaborate with other engineers to build, evaluate, and iterate on AI-powered systems
Solve real-world problems through well-architected, testable, and observable solutions
Evaluate and monitor LLM models in production, including A/B testing and observability practices
Generate and use synthetic datasets for evaluation or testing

Requirements

What you’ll need

Must be a Citizen or Green Card Holder and live in the United States
B.S. degree in Computer Science (or equivalent field) or 10+ years of experience in the software development / AI field
7+ years of overall software development experience
2+ years of hands-on experience working closely with Generative AI systems
Understanding of strengths and weaknesses of various foundational models
Advanced proficiency in Python, especially with AI/ML libraries and tooling
Expertise in prompt engineering, including structured prompt design and optimization
Experience creating agentic workflows and using orchestration frameworks (e.g., LangChain, LangGraph)
Exposure to human-in-the-loop (HITL) workflows and agent UX considerations
Experience building chat-based UX features
Solid understanding of best practices in context engineering
Demonstrated ability to evaluate and monitor LLM models in production, including A/B testing and observability practices
Experience generating and using synthetic datasets for evaluation or testing
Startup experience—comfortable wearing multiple hats and working with ambiguity
Outstanding communication skills—clear, concise, and effective in both writing and speech
Experience integrating agents via Model Context Protocol (MCP) (nice to have)
Experience with fine-tuning open-source LLMs and managing LLM Ops workflows (nice to have)
Familiarity with AWS, voice APIs, or multi-modal LLMs (nice to have)