Full-Time

Principal AI Engineer

Posted on 9/20/2025

Kizen

Kizen

51-200 employees

AI-driven sales, marketing, service platform

Compensation Overview

$250k - $350k/yr

+ Equity

Austin, TX, USA

Hybrid

Hybrid role with at least 4 days in office; open to Austin or Los Angeles office.

Category
AI & Machine Learning (2)
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Requirements
  • Bachelor's or Master's degree in Computer Science, Software Engineering, or a related field
  • 8+ years of backend engineering experience with Django, Kafka, and PostgreSQL
  • 4+ years of hands-on experience building and deploying machine learning systems
  • Proven track record of implementing production Retrieval-Augmented Generation systems at scale
  • Strong experience in product management, including work estimation and roadmap planning
  • Experience building solutions at scale with large enterprise data in healthcare, finance, or banking sectors.
  • Expert-level Python development skills with Django experience
  • Deep understanding of distributed systems and message queuing using message broker systems (e.g., Kafka)
  • Advanced PostgreSQL knowledge, including optimization for AI workloads
  • Experience building and optimizing retrieval-augmented generation (RAG) systems
  • Experience architecting and implementing multi-agent AI systems
  • Knowledge of deep learning frameworks (PyTorch or TensorFlow) and NLP, particularly transformer architectures
  • Experience with cloud platforms (AWS preferred) and containerization (Docker, Kubernetes)
  • Experience building solutions using pre-trained LLMs (OpenAI, Claude, Llama, etc.)
  • Strong background in MLOps practices and tools, including platforms like Langfuse or LiteLLM
  • Proficiency in writing clean, well-documented code and troubleshooting complex issues
  • Experience in testing and validating products and communicating results with stakeholders
  • Experience applying graph algorithms to machine learning problems
  • Strong experience with modern NLP techniques and transformer architectures
  • Knowledge of evaluation metrics for NLP system performance
  • Solid foundation in probability theory and statistical inference
  • Experience with statistical modeling and hypothesis testing
  • Understanding of sampling methods and experimental design
  • Proven experience designing and implementing scalable LLM-powered systems in production environments
  • Deep understanding of LLM orchestration and optimization techniques for high-throughput applications
  • Experience with prompt engineering, fine-tuning, and context window management for optimal LLM performance
  • Demonstrated expertise in LLM fine-tuning methodologies, including RLHF, PEFT, and LoRA techniques
  • Experience building data collection pipelines for LLM training and fine-tuning
  • Knowledge of efficient usage strategies, cost optimization for LLM API consumption, and performance optimization of large-scale deployments.
  • Experience implementing LLM caching mechanisms and vector store optimizations
  • Expertise in designing fault-tolerant LLM architectures with appropriate fallback mechanisms
  • Understanding of techniques to reduce latency in LLM-powered applications
  • Knowledge of strategies for handling data privacy and security in LLM applications
  • Knowledge of model monitoring and evaluation techniques
  • Experience designing and implementing robust user feedback collection systems for AI applications
  • Knowledge of feedback aggregation and analysis techniques to identify patterns and improvement areas
  • Experience building systems that leverage user feedback for continuous LLM improvement
  • Understanding of human-in-the-loop approaches for refining AI system outputs
  • Experience with A/B testing frameworks to evaluate AI system changes
  • Ability to translate user feedback into actionable model improvements
  • Experience implementing evaluation frameworks to measure AI system quality and performance
  • Demonstrated ability to lead technical initiatives and architectural decisions
  • Experience managing technical product roadmaps and providing accurate work estimations
  • Strong problem-solving skills and ability to work independently on complex projects
  • Strategic thinking ability to balance immediate solutions with long-term scalability
  • Excellent collaboration skills when working with cross-functional teams
  • Excellent written and verbal communication skills in English
  • Driven, self-motivated, adaptable, empathetic, energetic, and detail-oriented
  • Experience with graph-based RAG systems
  • Contributions to open-source projects in backend or AI domains
  • Experience with streaming data processing at scale
  • Deep interest in emerging AI technologies and their practical applications
  • Strong mentoring capabilities to guide and develop team members
  • Ability to work in our Los Angeles or Austin office
Responsibilities
  • Lead the design and implementation of production-ready RAG systems that integrate seamlessly with our backend infrastructure using Django, Kafka, PostgreSQL, and Clickhouse
  • Architect multi-agent AI systems that operate effectively within our platform's constraints and understand business value implications.
  • Drive product strategy by providing accurate work estimations and technical roadmaps with minimal supervision.
  • Design and implement sophisticated vector search solutions, including graph-based RAG systems
  • Architect and build highly scalable LLM-powered systems that can handle enterprise-level workloads
  • Lead LLM fine-tuning initiatives to customize models for specific business domains and use cases
  • Design and implement user feedback systems to collect, analyze, and incorporate insights for continuous improvement
  • Optimize LLM performance, cost, and reliability in production environments
  • Establish MLOps best practices using platforms like Langfuse or LiteLLM to ensure robust model monitoring and evaluation
  • Mentor and develop junior engineers in AI/ML best practices
  • Collaborate with cross-functional teams to translate business requirements into technical solutions
  • Lead system architecture decisions and technical direction for AI initiatives
  • Evaluate emerging AI technologies for potential adoption
Desired Qualifications
  • Experience with graph-based RAG systems
  • Contributions to open-source projects in backend or AI domains
  • Experience with streaming data processing at scale
  • Deep interest in emerging AI technologies and their practical applications
  • Strong mentoring capabilities to guide and develop team members
  • Ability to work in our Los Angeles or Austin office

Kizen provides a SaaS platform that helps sales, marketing, and service teams work more efficiently by unifying data, smart automation, and AI into a personalized engagement engine named Zoe. The product lets teams manage tasks, route leads, measure performance, and coordinate onboarding, all without requiring custom code or heavy programming. Zoe operates as a cloud-based platform that connects data sources and automates workflows to improve engagement and outcomes across the customer lifecycle. What sets Kizen apart is its focus on relieving teams from complex systems, enabling rapid idea deployment and iteration through AI-driven automation and data integration, rather than relying on heavy IT customization. The company targets a broad range of industries and aims to drive faster, smarter growth for businesses while contributing to social good by helping organizations prosper more efficiently and effectively.

Company Size

51-200

Company Stage

Seed

Total Funding

$12M

Headquarters

Austin, Texas

Founded

2018

Simplify Jobs

Simplify's Take

What believers are saying

  • Gartner's 2026 Magic Quadrant names no-code AI platforms like Kizen Leaders.
  • 81% employee satisfaction exceeds U.S. average, boosting talent retention.
  • HubSpot Q1 2026 survey shows 68% mid-market firms investing in AI CRM.

What critics are saying

  • Salesforce Agentforce erodes no-code CRM share via Einstein lock-in within 6 months.
  • Microsoft Copilot commoditizes Ultimate AI Assistant through Dynamics bundles in 3 months.
  • OpenAI GPT-5 APIs enable superior custom agents, obsoleting Kizen in 12 months.

What makes Kizen unique

  • Kizen builds first GenAI enterprise application platform founded in 2018.
  • AI-native no-code CRM enables agent orchestration with SOC 2, HIPAA compliance.
  • Ultimate AI Assistant cuts insurance admin from 45 to 3 minutes per client.

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Benefits

Health Insurance

Hybrid Work Options

Company Equity

Professional Development Budget

Paid Vacation

Growth & Insights and Company News

Headcount

6 month growth

-1%

1 year growth

0%

2 year growth

-5%
Cision
Nov 22nd, 2024
Kizen Launches New Platform & Unveils Its New Brand Identity

Kizen launches new platform & unveils its new brand identity.

Kizen
Sep 13th, 2022
Kizen Technologies Inc received financing of $12M in seed round on Sep 13th 22'.

Kizen, an enterprise business platform facilitating collaboration, intelligent automation, and personalized insights, today announced the closing of a $12 million seed round.

Kizen
Mar 21st, 2022
Kizen Technologies Inc launches national survey of knowledge workers

Austin, Texas (March 31, 2022) - Kizen, the makers of a no-code data platform and intelligent business assistant, today released the results of a new national survey of knowledge workers which revealed fresh insights about worker satisfaction, burnout and the future of work.

INACTIVE