2 months ago
Responsibilities
- Lead end-to-end technical engagements with enterprise customers and own delivery quality, stakeholder relationships, risks, escalations, workplans, and outcomes.
- Lead pre-sales and proof-of-concept engagements, customer workshops, executive briefings, and technical deep-dives.
- Design and build agentic AI systems, multi-agent frameworks, LLM-powered applications, RAG pipelines, and enterprise workflow automations.
- Implement fine-tuning, prompt engineering, function calling, tool use, guardrails, evaluation frameworks, and responsible AI controls.
- Own the AI application lifecycle from problem framing and data exploration through model development, API integration, and production deployment.
- Build scalable backend services and APIs and integrate AI models into cloud, on-premises, and hybrid customer environments.
- Develop ML pipelines and LLMOps infrastructure supporting model improvement and production monitoring.
- Coordinate engineers, program managers, and solution architects across concurrent delivery workstreams.
- Set technical direction, review outputs, shape architecture decisions, and maintain engineering quality.
- Mentor junior ML engineers and solution engineers and collaborate with product and engineering teams on platform improvements and roadmap input.
Requirements
- 8+ years of hands-on AI/ML engineering experience, including end-to-end model development and production deployment.
- Experience leading technical delivery across complex, multi-stakeholder enterprise engagements.
- Experience building LLM-powered applications such as RAG pipelines, agentic workflows, or fine-tuned models.
- Strong Python engineering skills and experience with PyTorch, TensorFlow, scikit-learn, and LLM tooling such as LangChain or LlamaIndex.
- Experience deploying AI services in AWS, Azure, GCP, on-premises environments, or Kubernetes.
- Understanding of prompt engineering, RAG, fine-tuning, RLHF, model evaluation, guardrails, and LLMOps.
- Grounding in classical machine learning and the ability to select appropriate approaches for different problems.
- Backend development experience with APIs, Docker, Kubernetes, and AI application delivery pipelines.
- Ability to manage concurrent workstreams, coordinate cross-functional teams, communicate with executives, and set direction amid ambiguity.
- Preferred: Kaggle or competitive ML experience; familiarity with H2O.ai, Wave, or H2O Document AI; regulated-industry AI deployment experience; exposure to tabular foundation models, AutoML, or enterprise ML platforms; prior customer-facing or field engineering experience.
Benefits
- Market-leading total rewards, remote-friendly culture, flexible working environment, world-class team, and career growth opportunities.
- The position is based in the San Francisco Bay Area and is described as remote-friendly; the posting includes the hashtag #LI-Hybrid.
Tech Stack
Categories
Forward Deployed
