2 months ago
Dallas, TX, USAStaff+
Responsibilities
- Lead end-to-end technical engagements with enterprise customers and serve as the senior point of accountability for delivery quality and outcomes.
- Manage concurrent engagement workstreams, including workplans, resourcing, milestones, risks, escalations, and cross-functional coordination.
- Build trusted relationships with customer data science, engineering, and executive stakeholders and translate business needs into technical direction.
- Lead pre-sales, proof-of-concept engagements, customer workshops, executive briefings, and technical deep-dives.
- Design and build agentic AI systems, multi-agent frameworks, RAG applications, fine-tuned models, and LLM-powered enterprise workflows.
- Implement 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 for continuous model improvement and production monitoring.
- Coordinate engineers, program managers, and solution architects; review technical outputs, shape architecture decisions, and maintain engineering quality.
- Mentor junior ML and solution engineers and collaborate with product and engineering teams on customer feedback and platform issues.
Requirements
- 8+ years of hands-on AI/ML engineering experience, including end-to-end model development and production deployment.
- Demonstrated 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.
- Deep understanding of prompt engineering, RAG, fine-tuning, RLHF, model evaluation, guardrails, and LLMOps.
- Strong classical machine learning knowledge and the ability to select appropriate modeling approaches.
- Backend development experience with REST APIs, Docker or Kubernetes containerization, and CI/CD pipelines for AI applications.
- Ability to manage multiple workstreams and coordinate cross-functional teams toward shared milestones.
- Strong executive communication skills spanning board-level briefings and technical design reviews.
- Ability to set direction amid ambiguity and evolving requirements.
- Preferred: Kaggle or competitive ML experience; familiarity with H2O.ai products, Wave, or H2O Document AI; regulated-industry AI deployment experience; exposure to tabular foundation models, AutoML, or enterprise ML platforms; and prior customer-facing or field engineering experience.
Benefits
- Market leader in total rewards.
- Remote-friendly culture with a flexible working environment; the position is based in Dallas, Texas and requires onsite customer interfacing.
- World-class team and career growth opportunities.
Tech Stack
Categories
Forward Deployed
