Electrical Project Engineer supporting MEP systems in commercial construction. Working closely with project teams on schedules and resolving construction issues in Arizona.
About the role
- Technical ambassador for Adobe working with global leaders on AI integration. Implementing integration patterns and ensuring maximum value from Adobe ecosystem.
Responsibilities
- Implement A2A integration patterns (APIs, webhooks, event streams, connectors) so customers can plug our AI capabilities into their existing applications and workflows.
- Create reusable SDKs, templates, and reference implementations that reduce friction for customers adopting our AI features.
- Act like a data analyst for model behavior : Query logs and metrics (SQL, notebooks, dashboards) to understand how models and prompts are performing in production.
- Investigate failure modes, edge cases, and drift (e.g., low-quality responses, latency spikes, low adoption).
- Design and maintain evaluation pipelines for AI features: Define success metrics and guardrails.
- Instrument AI features with strong observability and testing: Logging of inputs/outputs with privacy in mind.
- Design and implement AI-backed services and APIs in Python using PyTorch or similar frameworks.
- Work with customer-facing teams to turn customer feedback and production data into prioritized improvements and provide clear, data-backed insights.
Requirements
- 8+ years of software engineering experience with 2+ years working with ML/AI or LLM-based applications
- Expertise in Python , including hands-on work with PyTorch or similar frameworks
- Familiarity with A2A (application-to-application) integration patterns : REST/gRPC APIs, webhooks, queues, or event-driven systems
- Comfortable with data-analyst-style work : Writing SQL and working with analytics tools/notebooks
- Experience with at least one cloud platform (AWS / GCP / Azure) and standard dev tooling (Git, CI/CD, Docker)
- Exposure to LLM applications (RAG, agents, prompt pipelines) and their evaluation
- Experience with logging/observability stacks (e.g., OpenTelemetry, Prometheus/Grafana, Datadog, etc.)
- Familiarity with MLOps/LLMOps concepts: evaluation harnesses, prompt/version management, feature flags, or canary rollouts
- Proven track record of shipping production features and iterating based on real-world feedback
- Strong communication skills with the ability to explain technical details and data insights to customers and non-ML stakeholders.
Benefits
- This role is eligible for bonus and equity
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