2 months ago
Dallas, TX, USAStaff+
Responsibilities
- Lead end-to-end technical delivery for multiple concurrent enterprise customer engagements, including workplans, resourcing, milestones, risks, and escalations.
- Build agentic AI systems, multi-agent frameworks, RAG pipelines, fine-tuned models, prompt-engineering workflows, function-calling integrations, and tool-use capabilities.
- Implement guardrails, evaluation frameworks, and responsible AI controls for reliable and safe production systems.
- Own the AI application lifecycle from problem framing and data exploration through model development, API integration, deployment, monitoring, and continuous improvement.
- 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 production model improvement and monitoring.
- Lead pre-sales, proof-of-concept engagements, customer workshops, executive briefings, and technical deep-dives.
- Coordinate engineers, program managers, solution architects, product teams, and engineering teams to align delivery and resolve platform issues.
- Review technical outputs, shape architecture decisions, set engineering quality standards, and mentor junior ML and solution engineers.
Requirements
- At least 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, LLMOps, and classical ML.
- Backend development experience with REST APIs, Docker, Kubernetes, and CI/CD pipelines for AI applications.
- Ability to manage concurrent workstreams, coordinate cross-functional teams, communicate with executive and technical stakeholders, and set direction amid ambiguity.
- Preferred experience includes Kaggle or competitive ML, H2O.ai products, Wave, H2O Document AI, regulated-industry AI deployments, tabular foundation models, AutoML, enterprise ML platforms, or customer-facing field engineering.
Benefits
- Remote-friendly culture and flexible working environment.
- Career growth and participation on a world-class team.
- Market-leading total rewards.
- The position is based in Dallas, Texas and is marked as hybrid.
Tech Stack
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