Kizen · 6 hours ago
Principal AI Engineer
Austin, Texas, United States
Full-time
Hybrid
Lead/Staff
$250K/yr - $350K/yr
8+ years expKizen is a fast-growing technology company focused on making AI work for people across various industries. They are seeking a Principal AI Engineer to architect and build sophisticated AI systems that integrate with backend infrastructure, focusing on generative AI and multi-agent architectures.
Artificial Intelligence (AI)Big DataCRMMarketing AutomationSales Automation
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
Qualification
Required
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 RAG 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
Preferred
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
Benefits
Hybrid Work Model
Career Growth Opportunities
Engaging Work Culture
Top-Tier Compensation
Equity Package
Healthcare Coverage
Professional Development Stipends
PTO
Company
Kizen
Kizen is a no-code and enterprise-grade CRM, insights, and operations platform.
51-200 employees
Funding
Current Stage
Growth StageTotal Funding
$12M2022-09-13Seed· $12M
Leadership Team
John Winner
CEO
Recent News
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