Kizen

Principal AI Engineer

LOS ANGELES, CAPosted 30+ days ago

Job summary

  • Job post source

    This job is directly from Kizen

  • Job overview

    The Principal AI Engineer at Kizen leads the design and development of advanced AI systems integrating generative AI and backend technologies to transform industries like healthcare and finance.

  • Responsibilities and impact

    The role involves architecting RAG and multi-agent AI systems, leading LLM fine-tuning, optimizing AI system performance, mentoring engineers, and collaborating across teams to align AI initiatives with business goals.

  • Compensation and benefits

    The position offers a base salary range of $200,000-$250,000 with an OTE of $250,000-$300,000, equity packages, healthcare coverage, PTO, professional development stipends, and a hybrid work model.

  • Experience and skills

    Candidates need 8+ years in backend engineering with Django, Kafka, PostgreSQL, 4+ years in ML system deployment, expertise in RAG systems, LLMs, Python, distributed systems, cloud platforms, and strong product management skills; preferred qualifications include graph-based RAG experience and mentoring capabilities.

  • Career development

    Kizen provides opportunities for professional growth, leadership in technical initiatives, and career advancement within a fast-growing, innovative company.

  • Work environment and culture

    Kizen promotes a startup culture valuing innovation, ownership, rapid iteration, diversity, inclusion, and a supportive, dynamic, collaborative workplace with hybrid work options.

  • Company information

    Kizen is a pioneering GenAI enterprise application builder focused on AI-driven solutions for healthcare, finance, HR, and enterprise applications, aiming to enhance business processes and human experiences.

  • Team overview

    The candidate will join a brilliant, fun, and collaborative engineering team focused on cutting-edge AI and backend system challenges.

  • Job location and travel

    The job is based in Kizen's Los Angeles or Austin offices with a hybrid work model.

  • Unique job features

    The role offers unique opportunities to work on groundbreaking AI technologies including RAG, multi-agent AI architectures, and LLM fine-tuning with real-world impact across multiple industries.

Company overview

Kizen is a technology company specializing in AI-powered business solutions designed to optimize customer relationship management (CRM), sales, and marketing processes. They generate revenue by offering cloud-based software and tools that help businesses automate workflows, analyze data, and enhance customer engagement. Founded in 2017, Kizen has positioned itself as a leader in leveraging artificial intelligence to deliver personalized and efficient business operations. Their innovative platform integrates seamlessly with existing systems, making it a popular choice for organizations aiming to modernize and scale their operations.

How to land this job

  • Position your resume to emphasize your extensive experience in backend engineering with Django, Kafka, and PostgreSQL, highlighting your expertise in architecting scalable AI systems and RAG implementations as described in the job requirements.

  • Focus on showcasing your leadership in technical strategy, product roadmap planning, and mentorship abilities, as Kizen values engineers who can lead AI initiatives and develop junior team members.

  • Apply through multiple channels including Kizen's corporate careers page and LinkedIn to maximize your application visibility and ensure your resume reaches the right recruiters and hiring managers.

  • Connect with current Principal AI Engineers or AI team leads at Kizen on LinkedIn; start conversations by referencing Kizen's mission to revolutionize industries with AI or asking about their experiences integrating multi-agent AI systems within enterprise platforms.

  • Optimize your resume for ATS by including key phrases from the job description such as 'retrieval-augmented generation (RAG)', 'multi-agent AI systems', 'LLM fine-tuning', 'MLOps', and specific technologies like 'Django', 'Kafka', and 'PostgreSQL' to pass initial screening algorithms.

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