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10+ years of software engineering AI/ML experience
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Proven leadership of production AI/ML systems at scale
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Deep expertise in LLM productionization. Including: RAG, finetuning, evaluation, guardrails, and model monitoring.
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Strong Python experience
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Experience with modern AI frameworks. Including: PyTorch, TensorFlow, JAX, Scikit-learn.
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Hands-on AI/MLOps experience. Including: CI/CD for ML, deployment automation, experiment tracking, and monitoring.
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Strong experience with cloud platforms. Including: AWS/GCP/Azure.
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Strong experience with Kubernetes and other distributed systems
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Experienced in building evaluation pipelines and adding observability instrumentation.
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Technical leadership by shaping the architectural direction across multiple teams.
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Define and own architecture for scalable AI/ML systems. Including:
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Inference pipelines Evaluation frameworks Model lifecycle workflows Monitoring and observability systems
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Translate business requirements into robust AI platform designs and delivery plans
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Make strategic decisions on:
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Model integrations and gateways Retrieval-augmented generation (RAG) approaches Evaluation methodologies Safety and guardrail systems
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Establish standards for model readiness, evaluation gates, rollout/rollback mechanisms, and drift detection
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Build and deploy production-grade LLM capabilities integrated into distributed systems with clear SLOs and telemetry
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Design scalable AI/MLOps and AIOps practices across training, testing, deployment, and monitoring
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Improve data pipelines, feature workflows, and lineage processes supporting model evaluation and inference
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Instrument tracing and model observability using OpenTelemetry and modern telemetry standards
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Own evaluation pipelines tracking latency, cost, accuracy, hallucination rates, and prompt/version drift
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Provide clear trade-off analyses balancing model performance, cost efficiency, safety, and maintainability
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Create clear, well-structured technical proposals that help guide executive decisions on investments and roadmap planning.
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Guide engineers in AI production, fostering good experimentation habits, and designing distributed systems.
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Boost engineering quality with thoughtful reviews, clear documentation, and standards built on solid practices.
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Shape the AI production architecture of a category-defining GenAI infrastructure company
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Define how enterprise-grade AI systems are observed, evaluated, and remediated
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Build mechanisms that scale beyond individual engineers
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Influence roadmap and platform strategy at a formative stage
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