Vice President, AI Product Engineer

Oaktree Capital Management•Los Angeles, CA

56d•$190,000 - $225,000

About The Position

Oaktree is seeking to hire an AI Product Engineer to join our Product Engineering group. 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. You'll work closely with other engineers, product thinkers, and internal stakeholders to build AI-native applications, intelligent copilots, RAG pipelines, and task-specific agents that solve real business problems. We're looking for someone who deeply understands the capabilities and constraints of modern AI-and knows how to use them to ship differentiated, high-leverage tools.

Requirements

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.

Nice To Haves

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.

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.