Eleven Recruiting
About Eleven Recruiting
We are a specialized technology staffing agency supporting professional and financial services companies. Why do we stand out in technology staffing? We listen and act as advisors for our candidates on how they can best add value, find interesting projects, and pave a path for career advancement. We advocate for best pay, diversity in tech, and best job-fit for every candidate we place.
Our client, an investment firm, is seeking an AI Product Owner to join their team in Los Angeles, CA! This role is for someone who thrives on turning the latest advances in LLMs and generative AI into working, useful products. You'll be part architect, part engineer, part product hacker-taking AI from idea to prototype to production at a high velocity.
Responsibilities : Prototype LLM-powered tools, copilots, and retrieval-based workflows using APIs, embeddings, and orchestration frameworks. Build full-stack AI features with robust infrastructure, including retrieval logic, prompt pipelines, caching, evaluation, and observability. Own the full AI lifecycle-from data preparation and experimentation to deployment and monitoring. Rapidly evaluate new models and frameworks (OpenAI, Claude, Gemini, open-source) to test what works and discard what doesn't. Monitor and improve system performance: accuracy, latency, hallucination, and user satisfaction. Translate ambiguous user or business needs into scoped, AI-capable features with clear goals and success metrics. Prioritize experiments and features based on impact, feasibility, and data-balancing speed with quality. Conduct internal or external user testing to validate product direction, gather feedback, and improve iteratively. Define KPIs and success criteria for AI-driven products, and track performance over time. Collaborate cross-functionally to align AI initiatives with product strategy, roadmap planning, and stakeholder needs. Act as a product owner for AI systems: making trade-offs, communicating rationale, and ensuring continuous learning from usage data. Ship fast, iterate smartly, and create real value-without waiting for perfect conditions and build systems that are observable, scalable, and evolve with usage. Help shape a company-wide culture of AI innovation and responsible deployment. Qualifications:
Bachelors degree is required. Master's in computer or data science preferred. 8+ years of software engineering or ML experience, with 3+ years focused on LLMs, NLP, or applied AI. Proven track record shipping production AI tools or platforms used by real users. Deep experience with Python, LangChain/OpenAI SDK, embeddings, RAG architecture, and vector DBs. Strong product sense-ability to define problems, measure impact, and drive iterative improvement. Hands-on experience optimizing LLM workflows for latency, cost, performance, and reliability. Ability to think from first principles and manage ambiguity across both technical and product domains. Experience with model fine-tuning, LoRA, or open-source model hosting (e.g., Hugging Face, Ollama, BentoML). Familiarity with agentic design, LangGraph, or task decomposition frameworks. Exposure to product analytics (Amplitude, Mixpanel, custom telemetry) and A/B testing of AI features. Experience building internal tools or workflows that increased efficiency or automation. Experience in designing user flows while simultaneously optimizing a prompt or embedding index.
Our client, an investment firm, is seeking an AI Product Owner to join their team in Los Angeles, CA! This role is for someone who thrives on turning the latest advances in LLMs and generative AI into working, useful products. You'll be part architect, part engineer, part product hacker-taking AI from idea to prototype to production at a high velocity.
Responsibilities : Prototype LLM-powered tools, copilots, and retrieval-based workflows using APIs, embeddings, and orchestration frameworks. Build full-stack AI features with robust infrastructure, including retrieval logic, prompt pipelines, caching, evaluation, and observability. Own the full AI lifecycle-from data preparation and experimentation to deployment and monitoring. Rapidly evaluate new models and frameworks (OpenAI, Claude, Gemini, open-source) to test what works and discard what doesn't. Monitor and improve system performance: accuracy, latency, hallucination, and user satisfaction. Translate ambiguous user or business needs into scoped, AI-capable features with clear goals and success metrics. Prioritize experiments and features based on impact, feasibility, and data-balancing speed with quality. Conduct internal or external user testing to validate product direction, gather feedback, and improve iteratively. Define KPIs and success criteria for AI-driven products, and track performance over time. Collaborate cross-functionally to align AI initiatives with product strategy, roadmap planning, and stakeholder needs. Act as a product owner for AI systems: making trade-offs, communicating rationale, and ensuring continuous learning from usage data. Ship fast, iterate smartly, and create real value-without waiting for perfect conditions and build systems that are observable, scalable, and evolve with usage. Help shape a company-wide culture of AI innovation and responsible deployment. Qualifications:
Bachelors degree is required. Master's in computer or data science preferred. 8+ years of software engineering or ML experience, with 3+ years focused on LLMs, NLP, or applied AI. Proven track record shipping production AI tools or platforms used by real users. Deep experience with Python, LangChain/OpenAI SDK, embeddings, RAG architecture, and vector DBs. Strong product sense-ability to define problems, measure impact, and drive iterative improvement. Hands-on experience optimizing LLM workflows for latency, cost, performance, and reliability. Ability to think from first principles and manage ambiguity across both technical and product domains. Experience with model fine-tuning, LoRA, or open-source model hosting (e.g., Hugging Face, Ollama, BentoML). Familiarity with agentic design, LangGraph, or task decomposition frameworks. Exposure to product analytics (Amplitude, Mixpanel, custom telemetry) and A/B testing of AI features. Experience building internal tools or workflows that increased efficiency or automation. Experience in designing user flows while simultaneously optimizing a prompt or embedding index.