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

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

PythonPyTorchSQLA2A integration patternsREST APIsgRPC APIsevent-driven systemsMLOpsLLMOpslogging/observability

strong communication skillsdata analysiscustomer feedback integrationprioritizationclear insights presentation